Setup

library(tidyverse)
library(janitor)
library(tools)
library(caret)
library(ROCR)
library(caret)
compute_auroc <- function(predictions, labels, title) {
    pred <- prediction(predictions, labels)
    auroc <- performance(pred, measure = 'auc')
    auroc <- auroc@y.values[[1]]
    perf <- performance(pred, 'tpr', 'fpr')
    plot(perf,
         main = title,
         xlim=c(0,1),
         ylim=c(0,1),
         colorize = FALSE)
    abline(a = 0,
           b = 1,
           col = 'red',
           lty = 2)
    text(0.8,
         0.25,
         paste('AUROC:', sprintf('%.3f', round(auroc, 3))))
}
compute_aupr <- function(predictions, labels, title) {
    n_black <- length(labels[labels == 1])
    n_total <- length(labels)
    pred <- prediction(predictions, labels)
    aupr <- performance(pred, measure = 'aucpr')
    aupr <- aupr@y.values[[1]]
    perf <- performance(pred, 'prec', 'rec')
    plot(perf,
         main = title,
         xlim=c(0,1),
         ylim=c(0,1),
         colorize = FALSE)
    abline(a = n_black/n_total,
           b = 0,
           col = 'red',
           lty = 2)
    text(0.2,
         0.2,
         paste('Null AUPR:', sprintf('%.3f', round(n_black / n_total, 3))))
    text(0.8,
         0.2,
         paste('AUPR:', sprintf('%.3f', round(aupr, 3))))
}
confusion_matrix <- function(df, column, threshold=0.5) {
    preds <- as.factor(if_else(df[column] >= threshold, 1, 0))
    confusionMatrix(preds, df$label, positive = '1')
}

Load data

irop_data <- read_csv('/Volumes/External/irop_data/irop_07092020.csv') %>%
    clean_names() %>%
    distinct(subject_id, .keep_all = TRUE) %>%
    select(subject_id, race)

── Column specification ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
cols(
  .default = col_character(),
  subjectVisitID = col_double(),
  birthWeight = col_double(),
  gestationalAgeWeeks = col_double(),
  gestationalAgeDays = col_double(),
  `PMA Weeks` = col_double(),
  `PMA Days` = col_double(),
  PMARaw = col_double(),
  `Session Followup` = col_double(),
  `Golden Reading Stage` = col_double(),
  bloodTaken = col_double(),
  salivaTaken = col_double()
)
ℹ Use `spec()` for the full column specifications.
Warning: 5380 parsing failures.
 row              col expected actual                                            file
1645 subjectVisitID   a double   NULL '/Volumes/External/irop_data/irop_07092020.csv'
1645 PMA Weeks        a double   NULL '/Volumes/External/irop_data/irop_07092020.csv'
1645 PMA Days         a double   NULL '/Volumes/External/irop_data/irop_07092020.csv'
1645 PMARaw           a double   NULL '/Volumes/External/irop_data/irop_07092020.csv'
1645 Session Followup a double   NULL '/Volumes/External/irop_data/irop_07092020.csv'
.... ................ ........ ...... ...............................................
See problems(...) for more details.
test_data <- read_csv('./out/datasets/test_data.csv') %>%
    select(subject_id, image_id)

── Column specification ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
cols(
  subject_id = col_character(),
  race = col_character(),
  variable = col_character(),
  value = col_character(),
  image_id = col_double(),
  fundus_location = col_character(),
  segmentation_location = col_character()
)
image_level <- read_csv('./out/probabilities/retcam_filtered_0.csv', col_types = cols()) %>%
    mutate(label = as.factor(if_else(str_detect(img_loc, 'black'), 1, 0)),
           image_id = as.numeric(file_path_sans_ext(basename(img_loc)))) %>%
    select(img_loc, image_id, label, retcam = probability) %>%
    bind_cols(select(read_csv('./out/probabilities/retcam_filtered_0_random.csv', col_types = cols()), retcam_random = probability)) %>%
    
    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_0_random.csv', col_types = cols()), filter_0_random = probability)) %>%
    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_0.csv', col_types = cols()), filter_0 = probability)) %>%
    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_0_binarized.csv', col_types = cols()), filter_0_binarized = probability)) %>%
    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_0_skeletonized.csv', col_types = cols()), filter_0_skeletonized = probability)) %>%
    
    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_50.csv', col_types = cols()), filter_50 = probability)) %>%
    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_50_binarized.csv', col_types = cols()), filter_50_binarized = probability)) %>%
    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_50_skeletonized.csv', col_types = cols()), filter_50_skeletonized = probability)) %>%

    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_100.csv', col_types = cols()), filter_100 = probability)) %>%
    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_100_binarized.csv', col_types = cols()), filter_100_binarized = probability)) %>%
    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_100_skeletonized.csv', col_types = cols()), filter_100_skeletonized = probability)) %>%

    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_150.csv', col_types = cols()), filter_150 = probability)) %>%
    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_150_binarized.csv', col_types = cols()), filter_150_binarized = probability)) %>%
    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_150_skeletonized.csv', col_types = cols()), filter_150_skeletonized = probability)) %>%

    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_200.csv', col_types = cols()), filter_200 = probability)) %>%
    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_200_binarized.csv', col_types = cols()), filter_200_binarized = probability)) %>%
    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_200_skeletonized.csv', col_types = cols()), filter_200_skeletonized = probability)) %>%

    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_210.csv', col_types = cols()), filter_210 = probability)) %>%
    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_210_binarized.csv', col_types = cols()), filter_210_binarized = probability)) %>%
    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_210_skeletonized.csv', col_types = cols()), filter_210_skeletonized = probability)) %>%

    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_220.csv', col_types = cols()), filter_220 = probability)) %>%
    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_220_binarized.csv', col_types = cols()), filter_220_binarized = probability)) %>%
    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_220_skeletonized.csv', col_types = cols()), filter_220_skeletonized = probability)) %>%

    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_230.csv', col_types = cols()), filter_230 = probability)) %>%
    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_230_binarized.csv', col_types = cols()), filter_230_binarized = probability)) %>%
    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_230_skeletonized.csv', col_types = cols()), filter_230_skeletonized = probability)) %>%

    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_240.csv', col_types = cols()), filter_240 = probability)) %>%
    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_240_binarized.csv', col_types = cols()), filter_240_binarized = probability)) %>%
    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_240_skeletonized.csv', col_types = cols()), filter_240_skeletonized = probability)) %>%

    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_250.csv', col_types = cols()), filter_250 = probability)) %>%
    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_250_binarized.csv', col_types = cols()), filter_250_binarized = probability)) %>%
    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_250_skeletonized.csv', col_types = cols()), filter_250_skeletonized = probability)) %>%

    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_257.csv', col_types = cols()), filter_257 = probability)) %>%
    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_257_binarized.csv', col_types = cols()), filter_257_binarized = probability)) %>%
    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_257_skeletonized.csv', col_types = cols()), filter_257_skeletonized = probability)) %>%

    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_0_10.csv', col_types = cols()), filter_10 = probability)) %>%
    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_0_10_binarized.csv', col_types = cols()), filter_10_binarized = probability)) %>%
    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_0_10_skeletonized.csv', col_types = cols()), filter_10_skeletonized = probability)) %>%

    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_75_150.csv', col_types = cols()), filter_75 = probability)) %>%
    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_75_150_binarized.csv', col_types = cols()), filter_75_binarized = probability)) %>%
    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_75_150_skeletonized.csv', col_types = cols()), filter_75_skeletonized = probability)) %>%
    
    inner_join(test_data, by = 'image_id') %>%
    select(subject_id, everything(), -img_loc)



subject_level <- image_level %>%
    group_by(subject_id) %>%
    mutate(across(c(-image_id, -label), median)) %>%
    ungroup() %>%
    distinct(subject_id, .keep_all = TRUE)

Image-level Analysis

RetCam Images

compute_aupr(image_level$retcam, image_level$label, 'PR: Raw RetCam Images')

compute_auroc(image_level$retcam, image_level$label, 'ROC: Raw RetCam Images')

confusion_matrix(image_level, 'retcam')
Confusion Matrix and Statistics

          Reference
Prediction   0   1
         0 819  10
         1   8 513
                                         
               Accuracy : 0.9867         
                 95% CI : (0.979, 0.9921)
    No Information Rate : 0.6126         
    P-Value [Acc > NIR] : <2e-16         
                                         
                  Kappa : 0.9719         
                                         
 Mcnemar's Test P-Value : 0.8137         
                                         
            Sensitivity : 0.9809         
            Specificity : 0.9903         
         Pos Pred Value : 0.9846         
         Neg Pred Value : 0.9879         
             Prevalence : 0.3874         
         Detection Rate : 0.3800         
   Detection Prevalence : 0.3859         
      Balanced Accuracy : 0.9856         
                                         
       'Positive' Class : 1              
                                         
compute_aupr(image_level$retcam_random, image_level$label, 'PR: Raw RetCam Images - Shuffled Labels')

compute_auroc(image_level$retcam_random, image_level$label, 'ROC: Raw RetCam Images - Shuffled Labels')

confusion_matrix(image_level, 'retcam_random')
Confusion Matrix and Statistics

          Reference
Prediction   0   1
         0 444 113
         1 383 410
                                          
               Accuracy : 0.6326          
                 95% CI : (0.6062, 0.6584)
    No Information Rate : 0.6126          
    P-Value [Acc > NIR] : 0.06902         
                                          
                  Kappa : 0.293           
                                          
 Mcnemar's Test P-Value : < 2e-16         
                                          
            Sensitivity : 0.7839          
            Specificity : 0.5369          
         Pos Pred Value : 0.5170          
         Neg Pred Value : 0.7971          
             Prevalence : 0.3874          
         Detection Rate : 0.3037          
   Detection Prevalence : 0.5874          
      Balanced Accuracy : 0.6604          
                                          
       'Positive' Class : 1               
                                          

Segmentations: zero all pixels < 0

compute_aupr(image_level$filter_0, image_level$label, 'PR: Segmentations: zero all pixels < 0')

compute_auroc(image_level$filter_0, image_level$label, 'ROC: Segmentations: zero all pixels < 0')

confusion_matrix(image_level, 'filter_0')
Confusion Matrix and Statistics

          Reference
Prediction   0   1
         0 758  74
         1  69 449
                                        
               Accuracy : 0.8941        
                 95% CI : (0.8764, 0.91)
    No Information Rate : 0.6126        
    P-Value [Acc > NIR] : <2e-16        
                                        
                  Kappa : 0.7764        
                                        
 Mcnemar's Test P-Value : 0.738         
                                        
            Sensitivity : 0.8585        
            Specificity : 0.9166        
         Pos Pred Value : 0.8668        
         Neg Pred Value : 0.9111        
             Prevalence : 0.3874        
         Detection Rate : 0.3326        
   Detection Prevalence : 0.3837        
      Balanced Accuracy : 0.8875        
                                        
       'Positive' Class : 1             
                                        
compute_aupr(image_level$filter_0_binarized, image_level$label, 'PR: Segmentations: zero all pixels < 0 and binarize')

compute_auroc(image_level$filter_0_binarized, image_level$label, 'ROC: Segmentations: zero all pixels < 0 and binarize')

confusion_matrix(image_level, 'filter_0_binarized')
Confusion Matrix and Statistics

          Reference
Prediction   0   1
         0 733  31
         1  94 492
                                          
               Accuracy : 0.9074          
                 95% CI : (0.8907, 0.9223)
    No Information Rate : 0.6126          
    P-Value [Acc > NIR] : < 2.2e-16       
                                          
                  Kappa : 0.8091          
                                          
 Mcnemar's Test P-Value : 2.932e-08       
                                          
            Sensitivity : 0.9407          
            Specificity : 0.8863          
         Pos Pred Value : 0.8396          
         Neg Pred Value : 0.9594          
             Prevalence : 0.3874          
         Detection Rate : 0.3644          
   Detection Prevalence : 0.4341          
      Balanced Accuracy : 0.9135          
                                          
       'Positive' Class : 1               
                                          
compute_aupr(image_level$filter_0_skeletonized, image_level$label, 'PR: Segmentations: zero all pixels < 0 and skeletonize')

compute_auroc(image_level$filter_0_skeletonized, image_level$label, 'ROC: Segmentations: zero all pixels < 0 and skeletonize')

confusion_matrix(image_level, 'filter_0_skeletonized')
Confusion Matrix and Statistics

          Reference
Prediction   0   1
         0 691  38
         1 136 485
                                          
               Accuracy : 0.8711          
                 95% CI : (0.8521, 0.8885)
    No Information Rate : 0.6126          
    P-Value [Acc > NIR] : < 2.2e-16       
                                          
                  Kappa : 0.7375          
                                          
 Mcnemar's Test P-Value : 1.93e-13        
                                          
            Sensitivity : 0.9273          
            Specificity : 0.8356          
         Pos Pred Value : 0.7810          
         Neg Pred Value : 0.9479          
             Prevalence : 0.3874          
         Detection Rate : 0.3593          
   Detection Prevalence : 0.4600          
      Balanced Accuracy : 0.8814          
                                          
       'Positive' Class : 1               
                                          
compute_aupr(image_level$filter_0_random, image_level$label, 'PR: Segmentations: zero all pixels < 0 - Shuffled Labels')

compute_auroc(image_level$filter_0_random, image_level$label, 'ROC: Segmentations: zero all pixels < 0 - Shuffled Labels')

confusion_matrix(image_level, 'filter_0_random')
Confusion Matrix and Statistics

          Reference
Prediction   0   1
         0 703 418
         1 124 105
                                          
               Accuracy : 0.5985          
                 95% CI : (0.5718, 0.6248)
    No Information Rate : 0.6126          
    P-Value [Acc > NIR] : 0.8619          
                                          
                  Kappa : 0.0567          
                                          
 Mcnemar's Test P-Value : <2e-16          
                                          
            Sensitivity : 0.20076         
            Specificity : 0.85006         
         Pos Pred Value : 0.45852         
         Neg Pred Value : 0.62712         
             Prevalence : 0.38741         
         Detection Rate : 0.07778         
   Detection Prevalence : 0.16963         
      Balanced Accuracy : 0.52541         
                                          
       'Positive' Class : 1               
                                          

Segmentations: zero all pixels < 50

compute_aupr(image_level$filter_50, image_level$label, 'PR: Segmentations: zero all pixels < 50')

compute_auroc(image_level$filter_50, image_level$label, 'ROC: Segmentations: zero all pixels < 50')

confusion_matrix(image_level, 'filter_50')
Confusion Matrix and Statistics

          Reference
Prediction   0   1
         0 588  63
         1 239 460
                                          
               Accuracy : 0.7763          
                 95% CI : (0.7531, 0.7983)
    No Information Rate : 0.6126          
    P-Value [Acc > NIR] : < 2.2e-16       
                                          
                  Kappa : 0.5561          
                                          
 Mcnemar's Test P-Value : < 2.2e-16       
                                          
            Sensitivity : 0.8795          
            Specificity : 0.7110          
         Pos Pred Value : 0.6581          
         Neg Pred Value : 0.9032          
             Prevalence : 0.3874          
         Detection Rate : 0.3407          
   Detection Prevalence : 0.5178          
      Balanced Accuracy : 0.7953          
                                          
       'Positive' Class : 1               
                                          
compute_aupr(image_level$filter_50_binarized, image_level$label, 'PR: Segmentations: zero all pixels < 50 and binarize')

compute_auroc(image_level$filter_50_binarized, image_level$label, 'ROC: Segmentations: zero all pixels < 50 and binarize')

confusion_matrix(image_level, 'filter_0_binarized')
Confusion Matrix and Statistics

          Reference
Prediction   0   1
         0 733  31
         1  94 492
                                          
               Accuracy : 0.9074          
                 95% CI : (0.8907, 0.9223)
    No Information Rate : 0.6126          
    P-Value [Acc > NIR] : < 2.2e-16       
                                          
                  Kappa : 0.8091          
                                          
 Mcnemar's Test P-Value : 2.932e-08       
                                          
            Sensitivity : 0.9407          
            Specificity : 0.8863          
         Pos Pred Value : 0.8396          
         Neg Pred Value : 0.9594          
             Prevalence : 0.3874          
         Detection Rate : 0.3644          
   Detection Prevalence : 0.4341          
      Balanced Accuracy : 0.9135          
                                          
       'Positive' Class : 1               
                                          
compute_aupr(image_level$filter_50_skeletonized, image_level$label, 'PR: Segmentations: zero all pixels < 50 and skeletonize')

compute_auroc(image_level$filter_50_skeletonized, image_level$label, 'ROC: Segmentations: zero all pixels < 50 and skeletonize')

confusion_matrix(image_level, 'filter_50_skeletonized')
Confusion Matrix and Statistics

          Reference
Prediction   0   1
         0 408  20
         1 419 503
                                          
               Accuracy : 0.6748          
                 95% CI : (0.6491, 0.6998)
    No Information Rate : 0.6126          
    P-Value [Acc > NIR] : 1.202e-06       
                                          
                  Kappa : 0.3991          
                                          
 Mcnemar's Test P-Value : < 2.2e-16       
                                          
            Sensitivity : 0.9618          
            Specificity : 0.4933          
         Pos Pred Value : 0.5456          
         Neg Pred Value : 0.9533          
             Prevalence : 0.3874          
         Detection Rate : 0.3726          
   Detection Prevalence : 0.6830          
      Balanced Accuracy : 0.7276          
                                          
       'Positive' Class : 1               
                                          

Segmentations: zero all pixels < 100

compute_aupr(image_level$filter_100, image_level$label, 'PR: Segmentations: zero all pixels < 100')

compute_auroc(image_level$filter_100, image_level$label, 'ROC: Segmentations: zero all pixels < 100')

confusion_matrix(image_level, 'filter_100')
Confusion Matrix and Statistics

          Reference
Prediction   0   1
         0 736 210
         1  91 313
                                         
               Accuracy : 0.777          
                 95% CI : (0.7539, 0.799)
    No Information Rate : 0.6126         
    P-Value [Acc > NIR] : < 2.2e-16      
                                         
                  Kappa : 0.5098         
                                         
 Mcnemar's Test P-Value : 1.036e-11      
                                         
            Sensitivity : 0.5985         
            Specificity : 0.8900         
         Pos Pred Value : 0.7748         
         Neg Pred Value : 0.7780         
             Prevalence : 0.3874         
         Detection Rate : 0.2319         
   Detection Prevalence : 0.2993         
      Balanced Accuracy : 0.7442         
                                         
       'Positive' Class : 1              
                                         
compute_aupr(image_level$filter_100_binarized, image_level$label, 'PR: Segmentations: zero all pixels < 100 and binarize')

compute_auroc(image_level$filter_100_binarized, image_level$label, 'ROC: Segmentations: zero all pixels < 100 and binarize')

confusion_matrix(image_level, 'filter_100_binarized')
Confusion Matrix and Statistics

          Reference
Prediction   0   1
         0 669 142
         1 158 381
                                          
               Accuracy : 0.7778          
                 95% CI : (0.7546, 0.7997)
    No Information Rate : 0.6126          
    P-Value [Acc > NIR] : <2e-16          
                                          
                  Kappa : 0.5344          
                                          
 Mcnemar's Test P-Value : 0.3865          
                                          
            Sensitivity : 0.7285          
            Specificity : 0.8089          
         Pos Pred Value : 0.7069          
         Neg Pred Value : 0.8249          
             Prevalence : 0.3874          
         Detection Rate : 0.2822          
   Detection Prevalence : 0.3993          
      Balanced Accuracy : 0.7687          
                                          
       'Positive' Class : 1               
                                          
compute_aupr(image_level$filter_100_skeletonized, image_level$label, 'PR: Segmentations: zero all pixels < 100 and skeletonize')

compute_auroc(image_level$filter_100_skeletonized, image_level$label, 'ROC: Segmentations: zero all pixels < 100 and skeletonize')

confusion_matrix(image_level, 'filter_100_skeletonized')
Confusion Matrix and Statistics

          Reference
Prediction   0   1
         0 632 144
         1 195 379
                                          
               Accuracy : 0.7489          
                 95% CI : (0.7249, 0.7718)
    No Information Rate : 0.6126          
    P-Value [Acc > NIR] : < 2.2e-16       
                                          
                  Kappa : 0.4803          
                                          
 Mcnemar's Test P-Value : 0.006615        
                                          
            Sensitivity : 0.7247          
            Specificity : 0.7642          
         Pos Pred Value : 0.6603          
         Neg Pred Value : 0.8144          
             Prevalence : 0.3874          
         Detection Rate : 0.2807          
   Detection Prevalence : 0.4252          
      Balanced Accuracy : 0.7444          
                                          
       'Positive' Class : 1               
                                          

Segmentations: zero all pixels < 150

compute_aupr(image_level$filter_150, image_level$label, 'PR: Segmentations: zero all pixels < 150')

compute_auroc(image_level$filter_150, image_level$label, 'ROC: Segmentations: zero all pixels < 150')

confusion_matrix(image_level, 'filter_150')
Confusion Matrix and Statistics

          Reference
Prediction   0   1
         0 625 118
         1 202 405
                                          
               Accuracy : 0.763           
                 95% CI : (0.7393, 0.7854)
    No Information Rate : 0.6126          
    P-Value [Acc > NIR] : < 2.2e-16       
                                          
                  Kappa : 0.5149          
                                          
 Mcnemar's Test P-Value : 3.487e-06       
                                          
            Sensitivity : 0.7744          
            Specificity : 0.7557          
         Pos Pred Value : 0.6672          
         Neg Pred Value : 0.8412          
             Prevalence : 0.3874          
         Detection Rate : 0.3000          
   Detection Prevalence : 0.4496          
      Balanced Accuracy : 0.7651          
                                          
       'Positive' Class : 1               
                                          
compute_aupr(image_level$filter_150_binarized, image_level$label, 'PR: Segmentations: zero all pixels < 150 and binarize')

compute_auroc(image_level$filter_150_binarized, image_level$label, 'ROC: Segmentations: zero all pixels < 150 and binarize')

confusion_matrix(image_level, 'filter_150_binarized')
Confusion Matrix and Statistics

          Reference
Prediction   0   1
         0 702 167
         1 125 356
                                          
               Accuracy : 0.7837          
                 95% CI : (0.7608, 0.8054)
    No Information Rate : 0.6126          
    P-Value [Acc > NIR] : < 2e-16         
                                          
                  Kappa : 0.5375          
                                          
 Mcnemar's Test P-Value : 0.01642         
                                          
            Sensitivity : 0.6807          
            Specificity : 0.8489          
         Pos Pred Value : 0.7401          
         Neg Pred Value : 0.8078          
             Prevalence : 0.3874          
         Detection Rate : 0.2637          
   Detection Prevalence : 0.3563          
      Balanced Accuracy : 0.7648          
                                          
       'Positive' Class : 1               
                                          
compute_aupr(image_level$filter_150_skeletonized, image_level$label, 'PR: Segmentations: zero all pixels < 150 and skeletonize')

compute_auroc(image_level$filter_150_skeletonized, image_level$label, 'ROC: Segmentations: zero all pixels < 150 and skeletonize')

confusion_matrix(image_level, 'filter_150_skeletonized')
Confusion Matrix and Statistics

          Reference
Prediction   0   1
         0 641 162
         1 186 361
                                         
               Accuracy : 0.7422         
                 95% CI : (0.718, 0.7654)
    No Information Rate : 0.6126         
    P-Value [Acc > NIR] : <2e-16         
                                         
                  Kappa : 0.4614         
                                         
 Mcnemar's Test P-Value : 0.2176         
                                         
            Sensitivity : 0.6902         
            Specificity : 0.7751         
         Pos Pred Value : 0.6600         
         Neg Pred Value : 0.7983         
             Prevalence : 0.3874         
         Detection Rate : 0.2674         
   Detection Prevalence : 0.4052         
      Balanced Accuracy : 0.7327         
                                         
       'Positive' Class : 1              
                                         

Segmentations: zero all pixels < 200

compute_aupr(image_level$filter_200, image_level$label, 'PR: Segmentations: zero all pixels < 200')

compute_auroc(image_level$filter_200, image_level$label, 'ROC: Segmentations: zero all pixels < 200')

confusion_matrix(image_level, 'filter_200')
Confusion Matrix and Statistics

          Reference
Prediction   0   1
         0 548 121
         1 279 402
                                         
               Accuracy : 0.7037         
                 95% CI : (0.6786, 0.728)
    No Information Rate : 0.6126         
    P-Value [Acc > NIR] : 1.64e-12       
                                         
                  Kappa : 0.4086         
                                         
 Mcnemar's Test P-Value : 4.16e-15       
                                         
            Sensitivity : 0.7686         
            Specificity : 0.6626         
         Pos Pred Value : 0.5903         
         Neg Pred Value : 0.8191         
             Prevalence : 0.3874         
         Detection Rate : 0.2978         
   Detection Prevalence : 0.5044         
      Balanced Accuracy : 0.7156         
                                         
       'Positive' Class : 1              
                                         
compute_aupr(image_level$filter_200_binarized, image_level$label, 'PR: Segmentations: zero all pixels < 200 and binarize')

compute_auroc(image_level$filter_200_binarized, image_level$label, 'ROC: Segmentations: zero all pixels < 200 and binarize')

confusion_matrix(image_level, 'filter_200_binarized')
Confusion Matrix and Statistics

          Reference
Prediction   0   1
         0 661 233
         1 166 290
                                          
               Accuracy : 0.7044          
                 95% CI : (0.6793, 0.7287)
    No Information Rate : 0.6126          
    P-Value [Acc > NIR] : 1.083e-12       
                                          
                  Kappa : 0.3623          
                                          
 Mcnemar's Test P-Value : 0.0009527       
                                          
            Sensitivity : 0.5545          
            Specificity : 0.7993          
         Pos Pred Value : 0.6360          
         Neg Pred Value : 0.7394          
             Prevalence : 0.3874          
         Detection Rate : 0.2148          
   Detection Prevalence : 0.3378          
      Balanced Accuracy : 0.6769          
                                          
       'Positive' Class : 1               
                                          
compute_aupr(image_level$filter_200_skeletonized, image_level$label, 'PR: Segmentations: zero all pixels < 200 and skeletonize')

compute_auroc(image_level$filter_200_skeletonized, image_level$label, 'ROC: Segmentations: zero all pixels < 200 and skeletonize')

confusion_matrix(image_level, 'filter_200_skeletonized')
Confusion Matrix and Statistics

          Reference
Prediction   0   1
         0 766 365
         1  61 158
                                          
               Accuracy : 0.6844          
                 95% CI : (0.6589, 0.7092)
    No Information Rate : 0.6126          
    P-Value [Acc > NIR] : 2.354e-08       
                                          
                  Kappa : 0.2557          
                                          
 Mcnemar's Test P-Value : < 2.2e-16       
                                          
            Sensitivity : 0.3021          
            Specificity : 0.9262          
         Pos Pred Value : 0.7215          
         Neg Pred Value : 0.6773          
             Prevalence : 0.3874          
         Detection Rate : 0.1170          
   Detection Prevalence : 0.1622          
      Balanced Accuracy : 0.6142          
                                          
       'Positive' Class : 1               
                                          

Segmentations: zero all pixels < 210

compute_aupr(image_level$filter_210, image_level$label, 'PR: Segmentations: zero all pixels < 210')

compute_auroc(image_level$filter_210, image_level$label, 'ROC: Segmentations: zero all pixels < 210')

confusion_matrix(image_level, 'filter_210')
Confusion Matrix and Statistics

          Reference
Prediction   0   1
         0 712 326
         1 115 197
                                          
               Accuracy : 0.6733          
                 95% CI : (0.6476, 0.6983)
    No Information Rate : 0.6126          
    P-Value [Acc > NIR] : 2.096e-06       
                                          
                  Kappa : 0.2566          
                                          
 Mcnemar's Test P-Value : < 2.2e-16       
                                          
            Sensitivity : 0.3767          
            Specificity : 0.8609          
         Pos Pred Value : 0.6314          
         Neg Pred Value : 0.6859          
             Prevalence : 0.3874          
         Detection Rate : 0.1459          
   Detection Prevalence : 0.2311          
      Balanced Accuracy : 0.6188          
                                          
       'Positive' Class : 1               
                                          
compute_aupr(image_level$filter_210_binarized, image_level$label, 'PR: Segmentations: zero all pixels < 210 and binarize')

compute_auroc(image_level$filter_210_binarized, image_level$label, 'ROC: Segmentations: zero all pixels < 210 and binarize')

confusion_matrix(image_level, 'filter_210_binarized')
Confusion Matrix and Statistics

          Reference
Prediction   0   1
         0 639 203
         1 188 320
                                          
               Accuracy : 0.7104          
                 95% CI : (0.6854, 0.7345)
    No Information Rate : 0.6126          
    P-Value [Acc > NIR] : 3.463e-14       
                                          
                  Kappa : 0.3866          
                                          
 Mcnemar's Test P-Value : 0.4789          
                                          
            Sensitivity : 0.6119          
            Specificity : 0.7727          
         Pos Pred Value : 0.6299          
         Neg Pred Value : 0.7589          
             Prevalence : 0.3874          
         Detection Rate : 0.2370          
   Detection Prevalence : 0.3763          
      Balanced Accuracy : 0.6923          
                                          
       'Positive' Class : 1               
                                          
compute_aupr(image_level$filter_210_skeletonized, image_level$label, 'PR: Segmentations: zero all pixels < 210 and skeletonize')

compute_auroc(image_level$filter_210_skeletonized, image_level$label, 'ROC: Segmentations: zero all pixels < 210 and skeletonize')

confusion_matrix(image_level, 'filter_210_skeletonized')
Confusion Matrix and Statistics

          Reference
Prediction   0   1
         0 562 160
         1 265 363
                                          
               Accuracy : 0.6852          
                 95% CI : (0.6597, 0.7099)
    No Information Rate : 0.6126          
    P-Value [Acc > NIR] : 1.699e-08       
                                          
                  Kappa : 0.3603          
                                          
 Mcnemar's Test P-Value : 4.541e-07       
                                          
            Sensitivity : 0.6941          
            Specificity : 0.6796          
         Pos Pred Value : 0.5780          
         Neg Pred Value : 0.7784          
             Prevalence : 0.3874          
         Detection Rate : 0.2689          
   Detection Prevalence : 0.4652          
      Balanced Accuracy : 0.6868          
                                          
       'Positive' Class : 1               
                                          

Segmentations: zero all pixels < 220

compute_aupr(image_level$filter_220, image_level$label, 'PR: Segmentations: zero all pixels < 220')

compute_auroc(image_level$filter_220, image_level$label, 'ROC: Segmentations: zero all pixels < 220')

confusion_matrix(image_level, 'filter_220')
Confusion Matrix and Statistics

          Reference
Prediction   0   1
         0 576 224
         1 251 299
                                         
               Accuracy : 0.6481         
                 95% CI : (0.622, 0.6736)
    No Information Rate : 0.6126         
    P-Value [Acc > NIR] : 0.003819       
                                         
                  Kappa : 0.2657         
                                         
 Mcnemar's Test P-Value : 0.232884       
                                         
            Sensitivity : 0.5717         
            Specificity : 0.6965         
         Pos Pred Value : 0.5436         
         Neg Pred Value : 0.7200         
             Prevalence : 0.3874         
         Detection Rate : 0.2215         
   Detection Prevalence : 0.4074         
      Balanced Accuracy : 0.6341         
                                         
       'Positive' Class : 1              
                                         
compute_aupr(image_level$filter_220_binarized, image_level$label, 'PR: Segmentations: zero all pixels < 220 and binarize')

compute_auroc(image_level$filter_220_binarized, image_level$label, 'ROC: Segmentations: zero all pixels < 220 and binarize')

confusion_matrix(image_level, 'filter_220_binarized')
Confusion Matrix and Statistics

          Reference
Prediction   0   1
         0 545 162
         1 282 361
                                          
               Accuracy : 0.6711          
                 95% CI : (0.6453, 0.6961)
    No Information Rate : 0.6126          
    P-Value [Acc > NIR] : 4.710e-06       
                                          
                  Kappa : 0.3351          
                                          
 Mcnemar's Test P-Value : 1.628e-08       
                                          
            Sensitivity : 0.6902          
            Specificity : 0.6590          
         Pos Pred Value : 0.5614          
         Neg Pred Value : 0.7709          
             Prevalence : 0.3874          
         Detection Rate : 0.2674          
   Detection Prevalence : 0.4763          
      Balanced Accuracy : 0.6746          
                                          
       'Positive' Class : 1               
                                          
compute_aupr(image_level$filter_220_skeletonized, image_level$label, 'PR: Segmentations: zero all pixels < 220 and skeletonize')

compute_auroc(image_level$filter_220_skeletonized, image_level$label, 'ROC: Segmentations: zero all pixels < 220 and skeletonize')

confusion_matrix(image_level, 'filter_220_skeletonized')
Confusion Matrix and Statistics

          Reference
Prediction   0   1
         0 800 450
         1  27  73
                                          
               Accuracy : 0.6467          
                 95% CI : (0.6205, 0.6722)
    No Information Rate : 0.6126          
    P-Value [Acc > NIR] : 0.005317        
                                          
                  Kappa : 0.1256          
                                          
 Mcnemar's Test P-Value : < 2.2e-16       
                                          
            Sensitivity : 0.13958         
            Specificity : 0.96735         
         Pos Pred Value : 0.73000         
         Neg Pred Value : 0.64000         
             Prevalence : 0.38741         
         Detection Rate : 0.05407         
   Detection Prevalence : 0.07407         
      Balanced Accuracy : 0.55347         
                                          
       'Positive' Class : 1               
                                          

Segmentations: zero all pixels < 230

compute_aupr(image_level$filter_230, image_level$label, 'PR: Segmentations: zero all pixels < 230')

compute_auroc(image_level$filter_230, image_level$label, 'ROC: Segmentations: zero all pixels < 230')

confusion_matrix(image_level, 'filter_230')
Confusion Matrix and Statistics

          Reference
Prediction   0   1
         0 464 160
         1 363 363
                                         
               Accuracy : 0.6126         
                 95% CI : (0.586, 0.6387)
    No Information Rate : 0.6126         
    P-Value [Acc > NIR] : 0.512          
                                         
                  Kappa : 0.2381         
                                         
 Mcnemar's Test P-Value : <2e-16         
                                         
            Sensitivity : 0.6941         
            Specificity : 0.5611         
         Pos Pred Value : 0.5000         
         Neg Pred Value : 0.7436         
             Prevalence : 0.3874         
         Detection Rate : 0.2689         
   Detection Prevalence : 0.5378         
      Balanced Accuracy : 0.6276         
                                         
       'Positive' Class : 1              
                                         
compute_aupr(image_level$filter_230_binarized, image_level$label, 'PR: Segmentations: zero all pixels < 230 and binarize')

compute_auroc(image_level$filter_230_binarized, image_level$label, 'ROC: Segmentations: zero all pixels < 230 and binarize')

confusion_matrix(image_level, 'filter_230_binarized')
Confusion Matrix and Statistics

          Reference
Prediction   0   1
         0 246  46
         1 581 477
                                          
               Accuracy : 0.5356          
                 95% CI : (0.5085, 0.5624)
    No Information Rate : 0.6126          
    P-Value [Acc > NIR] : 1               
                                          
                  Kappa : 0.1764          
                                          
 Mcnemar's Test P-Value : <2e-16          
                                          
            Sensitivity : 0.9120          
            Specificity : 0.2975          
         Pos Pred Value : 0.4509          
         Neg Pred Value : 0.8425          
             Prevalence : 0.3874          
         Detection Rate : 0.3533          
   Detection Prevalence : 0.7837          
      Balanced Accuracy : 0.6048          
                                          
       'Positive' Class : 1               
                                          
compute_aupr(image_level$filter_230_skeletonized, image_level$label, 'PR: Segmentations: zero all pixels < 230 and skeletonize')

compute_auroc(image_level$filter_230_skeletonized, image_level$label, 'ROC: Segmentations: zero all pixels < 230 and skeletonize')

confusion_matrix(image_level, 'filter_230_skeletonized')
Confusion Matrix and Statistics

          Reference
Prediction   0   1
         0 365 110
         1 462 413
                                          
               Accuracy : 0.5763          
                 95% CI : (0.5494, 0.6028)
    No Information Rate : 0.6126          
    P-Value [Acc > NIR] : 0.997           
                                          
                  Kappa : 0.2056          
                                          
 Mcnemar's Test P-Value : <2e-16          
                                          
            Sensitivity : 0.7897          
            Specificity : 0.4414          
         Pos Pred Value : 0.4720          
         Neg Pred Value : 0.7684          
             Prevalence : 0.3874          
         Detection Rate : 0.3059          
   Detection Prevalence : 0.6481          
      Balanced Accuracy : 0.6155          
                                          
       'Positive' Class : 1               
                                          

Segmentations: zero all pixels < 240

compute_aupr(image_level$filter_240, image_level$label, 'PR: Segmentations: zero all pixels < 240')

compute_auroc(image_level$filter_240, image_level$label, 'ROC: Segmentations: zero all pixels < 240')

confusion_matrix(image_level, 'filter_240')
Confusion Matrix and Statistics

          Reference
Prediction   0   1
         0 414 183
         1 413 340
                                          
               Accuracy : 0.5585          
                 95% CI : (0.5316, 0.5852)
    No Information Rate : 0.6126          
    P-Value [Acc > NIR] : 1               
                                          
                  Kappa : 0.1394          
                                          
 Mcnemar's Test P-Value : <2e-16          
                                          
            Sensitivity : 0.6501          
            Specificity : 0.5006          
         Pos Pred Value : 0.4515          
         Neg Pred Value : 0.6935          
             Prevalence : 0.3874          
         Detection Rate : 0.2519          
   Detection Prevalence : 0.5578          
      Balanced Accuracy : 0.5754          
                                          
       'Positive' Class : 1               
                                          
compute_aupr(image_level$filter_240_binarized, image_level$label, 'PR: Segmentations: zero all pixels < 240 and binarize')

compute_auroc(image_level$filter_240_binarized, image_level$label, 'ROC: Segmentations: zero all pixels < 240 and binarize')

confusion_matrix(image_level, 'filter_240_binarized')
Confusion Matrix and Statistics

          Reference
Prediction   0   1
         0 258  83
         1 569 440
                                       
               Accuracy : 0.517        
                 95% CI : (0.49, 0.544)
    No Information Rate : 0.6126       
    P-Value [Acc > NIR] : 1            
                                       
                  Kappa : 0.1309       
                                       
 Mcnemar's Test P-Value : <2e-16       
                                       
            Sensitivity : 0.8413       
            Specificity : 0.3120       
         Pos Pred Value : 0.4361       
         Neg Pred Value : 0.7566       
             Prevalence : 0.3874       
         Detection Rate : 0.3259       
   Detection Prevalence : 0.7474       
      Balanced Accuracy : 0.5766       
                                       
       'Positive' Class : 1            
                                       
compute_aupr(image_level$filter_240_skeletonized, image_level$label, 'PR: Segmentations: zero all pixels < 240 and skeletonize')

compute_auroc(image_level$filter_240_skeletonized, image_level$label, 'ROC: Segmentations: zero all pixels < 240 and skeletonize')

confusion_matrix(image_level, 'filter_240_skeletonized')
Confusion Matrix and Statistics

          Reference
Prediction   0   1
         0 279 103
         1 548 420
                                          
               Accuracy : 0.5178          
                 95% CI : (0.4907, 0.5447)
    No Information Rate : 0.6126          
    P-Value [Acc > NIR] : 1               
                                          
                  Kappa : 0.1214          
                                          
 Mcnemar's Test P-Value : <2e-16          
                                          
            Sensitivity : 0.8031          
            Specificity : 0.3374          
         Pos Pred Value : 0.4339          
         Neg Pred Value : 0.7304          
             Prevalence : 0.3874          
         Detection Rate : 0.3111          
   Detection Prevalence : 0.7170          
      Balanced Accuracy : 0.5702          
                                          
       'Positive' Class : 1               
                                          

Segmentations: zero all pixels < 250

compute_aupr(image_level$filter_250, image_level$label, 'PR: Segmentations: zero all pixels < 250')

compute_auroc(image_level$filter_250, image_level$label, 'ROC: Segmentations: zero all pixels < 250')

confusion_matrix(image_level, 'filter_250')
Confusion Matrix and Statistics

          Reference
Prediction   0   1
         0  51   9
         1 776 514
                                         
               Accuracy : 0.4185         
                 95% CI : (0.392, 0.4454)
    No Information Rate : 0.6126         
    P-Value [Acc > NIR] : 1              
                                         
                  Kappa : 0.035          
                                         
 Mcnemar's Test P-Value : <2e-16         
                                         
            Sensitivity : 0.98279        
            Specificity : 0.06167        
         Pos Pred Value : 0.39845        
         Neg Pred Value : 0.85000        
             Prevalence : 0.38741        
         Detection Rate : 0.38074        
   Detection Prevalence : 0.95556        
      Balanced Accuracy : 0.52223        
                                         
       'Positive' Class : 1              
                                         
compute_aupr(image_level$filter_250_binarized, image_level$label, 'PR: Segmentations: zero all pixels < 250 and binarize')

compute_auroc(image_level$filter_250_binarized, image_level$label, 'ROC: Segmentations: zero all pixels < 250 and binarize')

confusion_matrix(image_level, 'filter_250_binarized')
Confusion Matrix and Statistics

          Reference
Prediction   0   1
         0  50   8
         1 777 515
                                         
               Accuracy : 0.4185         
                 95% CI : (0.392, 0.4454)
    No Information Rate : 0.6126         
    P-Value [Acc > NIR] : 1              
                                         
                  Kappa : 0.0356         
                                         
 Mcnemar's Test P-Value : <2e-16         
                                         
            Sensitivity : 0.98470        
            Specificity : 0.06046        
         Pos Pred Value : 0.39861        
         Neg Pred Value : 0.86207        
             Prevalence : 0.38741        
         Detection Rate : 0.38148        
   Detection Prevalence : 0.95704        
      Balanced Accuracy : 0.52258        
                                         
       'Positive' Class : 1              
                                         
compute_aupr(image_level$filter_250_skeletonized, image_level$label, 'PR: Segmentations: zero all pixels < 250 and skeletonize')

compute_auroc(image_level$filter_250_skeletonized, image_level$label, 'ROC: Segmentations: zero all pixels < 250 and skeletonize')

confusion_matrix(image_level, 'filter_250_skeletonized')
Confusion Matrix and Statistics

          Reference
Prediction   0   1
         0  53   9
         1 774 514
                                          
               Accuracy : 0.42            
                 95% CI : (0.3935, 0.4468)
    No Information Rate : 0.6126          
    P-Value [Acc > NIR] : 1               
                                          
                  Kappa : 0.0369          
                                          
 Mcnemar's Test P-Value : <2e-16          
                                          
            Sensitivity : 0.98279         
            Specificity : 0.06409         
         Pos Pred Value : 0.39907         
         Neg Pred Value : 0.85484         
             Prevalence : 0.38741         
         Detection Rate : 0.38074         
   Detection Prevalence : 0.95407         
      Balanced Accuracy : 0.52344         
                                          
       'Positive' Class : 1               
                                          

Segmentations: zero all pixels < 257

compute_aupr(image_level$filter_257, image_level$label, 'PR: Segmentations: zero all pixels < 257')

compute_auroc(image_level$filter_257, image_level$label, 'ROC: Segmentations: zero all pixels < 257')

confusion_matrix(image_level, 'filter_257')
Warning in confusionMatrix.default(preds, df$label, positive = "1") :
  Levels are not in the same order for reference and data. Refactoring data to match.
Confusion Matrix and Statistics

          Reference
Prediction   0   1
         0 827 523
         1   0   0
                                         
               Accuracy : 0.6126         
                 95% CI : (0.586, 0.6387)
    No Information Rate : 0.6126         
    P-Value [Acc > NIR] : 0.512          
                                         
                  Kappa : 0              
                                         
 Mcnemar's Test P-Value : <2e-16         
                                         
            Sensitivity : 0.0000         
            Specificity : 1.0000         
         Pos Pred Value :    NaN         
         Neg Pred Value : 0.6126         
             Prevalence : 0.3874         
         Detection Rate : 0.0000         
   Detection Prevalence : 0.0000         
      Balanced Accuracy : 0.5000         
                                         
       'Positive' Class : 1              
                                         
compute_aupr(image_level$filter_257_binarized, image_level$label, 'PR: Segmentations: zero all pixels < 257 and binarize')

compute_auroc(image_level$filter_257_binarized, image_level$label, 'ROC: Segmentations: zero all pixels < 257 and binarize')

confusion_matrix(image_level, 'filter_257_binarized')
Warning in confusionMatrix.default(preds, df$label, positive = "1") :
  Levels are not in the same order for reference and data. Refactoring data to match.
Confusion Matrix and Statistics

          Reference
Prediction   0   1
         0 827 523
         1   0   0
                                         
               Accuracy : 0.6126         
                 95% CI : (0.586, 0.6387)
    No Information Rate : 0.6126         
    P-Value [Acc > NIR] : 0.512          
                                         
                  Kappa : 0              
                                         
 Mcnemar's Test P-Value : <2e-16         
                                         
            Sensitivity : 0.0000         
            Specificity : 1.0000         
         Pos Pred Value :    NaN         
         Neg Pred Value : 0.6126         
             Prevalence : 0.3874         
         Detection Rate : 0.0000         
   Detection Prevalence : 0.0000         
      Balanced Accuracy : 0.5000         
                                         
       'Positive' Class : 1              
                                         
compute_aupr(image_level$filter_257_skeletonized, image_level$label, 'PR: Segmentations: zero all pixels < 257 and skeletonize')

compute_auroc(image_level$filter_257_skeletonized, image_level$label, 'ROC: Segmentations: zero all pixels < 257 and skeletonize')

confusion_matrix(image_level, 'filter_257_skeletonized')
Warning in confusionMatrix.default(preds, df$label, positive = "1") :
  Levels are not in the same order for reference and data. Refactoring data to match.
Confusion Matrix and Statistics

          Reference
Prediction   0   1
         0 827 523
         1   0   0
                                         
               Accuracy : 0.6126         
                 95% CI : (0.586, 0.6387)
    No Information Rate : 0.6126         
    P-Value [Acc > NIR] : 0.512          
                                         
                  Kappa : 0              
                                         
 Mcnemar's Test P-Value : <2e-16         
                                         
            Sensitivity : 0.0000         
            Specificity : 1.0000         
         Pos Pred Value :    NaN         
         Neg Pred Value : 0.6126         
             Prevalence : 0.3874         
         Detection Rate : 0.0000         
   Detection Prevalence : 0.0000         
      Balanced Accuracy : 0.5000         
                                         
       'Positive' Class : 1              
                                         

Segmentations: zero all pixels > 10

compute_aupr(image_level$filter_10, image_level$label, 'PR: Segmentations: zero all pixels < 10')

compute_auroc(image_level$filter_10, image_level$label, 'ROC: Segmentations: zero all pixels < 10')

confusion_matrix(image_level, 'filter_10')
Confusion Matrix and Statistics

          Reference
Prediction   0   1
         0 763 196
         1  64 327
                                          
               Accuracy : 0.8074          
                 95% CI : (0.7853, 0.8281)
    No Information Rate : 0.6126          
    P-Value [Acc > NIR] : < 2e-16         
                                          
                  Kappa : 0.5745          
                                          
 Mcnemar's Test P-Value : 4.5e-16         
                                          
            Sensitivity : 0.6252          
            Specificity : 0.9226          
         Pos Pred Value : 0.8363          
         Neg Pred Value : 0.7956          
             Prevalence : 0.3874          
         Detection Rate : 0.2422          
   Detection Prevalence : 0.2896          
      Balanced Accuracy : 0.7739          
                                          
       'Positive' Class : 1               
                                          
compute_aupr(image_level$filter_10_binarized, image_level$label, 'PR: Segmentations: zero all pixels < 10 and binarize')

compute_auroc(image_level$filter_10_binarized, image_level$label, 'ROC: Segmentations: zero all pixels < 10 and binarize')

confusion_matrix(image_level, 'filter_10_binarized')
Confusion Matrix and Statistics

          Reference
Prediction   0   1
         0 621   7
         1 206 516
                                          
               Accuracy : 0.8422          
                 95% CI : (0.8217, 0.8613)
    No Information Rate : 0.6126          
    P-Value [Acc > NIR] : < 2.2e-16       
                                          
                  Kappa : 0.6893          
                                          
 Mcnemar's Test P-Value : < 2.2e-16       
                                          
            Sensitivity : 0.9866          
            Specificity : 0.7509          
         Pos Pred Value : 0.7147          
         Neg Pred Value : 0.9889          
             Prevalence : 0.3874          
         Detection Rate : 0.3822          
   Detection Prevalence : 0.5348          
      Balanced Accuracy : 0.8688          
                                          
       'Positive' Class : 1               
                                          
compute_aupr(image_level$filter_10_skeletonized, image_level$label, 'PR: Segmentations: zero all pixels < 10 and skeletonize')

compute_auroc(image_level$filter_10_skeletonized, image_level$label, 'ROC: Segmentations: zero all pixels < 10 and skeletonize')

confusion_matrix(image_level, 'filter_10_skeletonized')
Confusion Matrix and Statistics

          Reference
Prediction   0   1
         0 696  43
         1 131 480
                                          
               Accuracy : 0.8711          
                 95% CI : (0.8521, 0.8885)
    No Information Rate : 0.6126          
    P-Value [Acc > NIR] : < 2.2e-16       
                                          
                  Kappa : 0.7366          
                                          
 Mcnemar's Test P-Value : 4.24e-11        
                                          
            Sensitivity : 0.9178          
            Specificity : 0.8416          
         Pos Pred Value : 0.7856          
         Neg Pred Value : 0.9418          
             Prevalence : 0.3874          
         Detection Rate : 0.3556          
   Detection Prevalence : 0.4526          
      Balanced Accuracy : 0.8797          
                                          
       'Positive' Class : 1               
                                          

Segmentations: zero all pixels < 75 and > 150

compute_aupr(image_level$filter_75, image_level$label, 'PR: Segmentations: zero all pixels < 75 and > 150')

compute_auroc(image_level$filter_75, image_level$label, 'ROC: Segmentations: zero all pixels < 75 and > 150')

confusion_matrix(image_level, 'filter_75')
Confusion Matrix and Statistics

          Reference
Prediction   0   1
         0 597  77
         1 230 446
                                          
               Accuracy : 0.7726          
                 95% CI : (0.7493, 0.7947)
    No Information Rate : 0.6126          
    P-Value [Acc > NIR] : < 2.2e-16       
                                          
                  Kappa : 0.5453          
                                          
 Mcnemar's Test P-Value : < 2.2e-16       
                                          
            Sensitivity : 0.8528          
            Specificity : 0.7219          
         Pos Pred Value : 0.6598          
         Neg Pred Value : 0.8858          
             Prevalence : 0.3874          
         Detection Rate : 0.3304          
   Detection Prevalence : 0.5007          
      Balanced Accuracy : 0.7873          
                                          
       'Positive' Class : 1               
                                          
compute_aupr(image_level$filter_75_binarized, image_level$label, 'PR: Segmentations: zero all pixels < 75 and > 150 and binarize')

compute_auroc(image_level$filter_75_binarized, image_level$label, 'ROC: Segmentations: zero all pixels < 75 and > 150 and binarize')

confusion_matrix(image_level, 'filter_75_binarized')
Confusion Matrix and Statistics

          Reference
Prediction   0   1
         0 545  69
         1 282 454
                                          
               Accuracy : 0.74            
                 95% CI : (0.7157, 0.7632)
    No Information Rate : 0.6126          
    P-Value [Acc > NIR] : < 2.2e-16       
                                          
                  Kappa : 0.4904          
                                          
 Mcnemar's Test P-Value : < 2.2e-16       
                                          
            Sensitivity : 0.8681          
            Specificity : 0.6590          
         Pos Pred Value : 0.6168          
         Neg Pred Value : 0.8876          
             Prevalence : 0.3874          
         Detection Rate : 0.3363          
   Detection Prevalence : 0.5452          
      Balanced Accuracy : 0.7635          
                                          
       'Positive' Class : 1               
                                          
compute_aupr(image_level$filter_75_skeletonized, image_level$label, 'PR: Segmentations: zero all pixels < 75 and > 150 and skeletonize')

compute_auroc(image_level$filter_75_skeletonized, image_level$label, 'ROC: Segmentations: zero all pixels < 75 and > 150 and skeletonize')

confusion_matrix(image_level, 'filter_75_skeletonized')
Confusion Matrix and Statistics

          Reference
Prediction   0   1
         0 726 224
         1 101 299
                                          
               Accuracy : 0.7593          
                 95% CI : (0.7355, 0.7819)
    No Information Rate : 0.6126          
    P-Value [Acc > NIR] : < 2.2e-16       
                                          
                  Kappa : 0.4699          
                                          
 Mcnemar's Test P-Value : 1.312e-11       
                                          
            Sensitivity : 0.5717          
            Specificity : 0.8779          
         Pos Pred Value : 0.7475          
         Neg Pred Value : 0.7642          
             Prevalence : 0.3874          
         Detection Rate : 0.2215          
   Detection Prevalence : 0.2963          
      Balanced Accuracy : 0.7248          
                                          
       'Positive' Class : 1               
                                          

Subject-level Analysis

RetCam Images

compute_aupr(subject_level$retcam, subject_level$label, 'PR: Raw RetCam Images')

compute_auroc(subject_level$retcam, subject_level$label, 'ROC: Raw RetCam Images')

confusion_matrix(subject_level, 'retcam')
Confusion Matrix and Statistics

          Reference
Prediction  0  1
         0 43  0
         1  0 27
                                     
               Accuracy : 1          
                 95% CI : (0.9487, 1)
    No Information Rate : 0.6143     
    P-Value [Acc > NIR] : 1.534e-15  
                                     
                  Kappa : 1          
                                     
 Mcnemar's Test P-Value : NA         
                                     
            Sensitivity : 1.0000     
            Specificity : 1.0000     
         Pos Pred Value : 1.0000     
         Neg Pred Value : 1.0000     
             Prevalence : 0.3857     
         Detection Rate : 0.3857     
   Detection Prevalence : 0.3857     
      Balanced Accuracy : 1.0000     
                                     
       'Positive' Class : 1          
                                     
compute_aupr(subject_level$retcam_random, subject_level$label, 'PR: Raw RetCam Images - Shuffled Labels')

compute_auroc(subject_level$retcam_random, subject_level$label, 'ROC: Raw RetCam Images - Shuffled Labels')

confusion_matrix(subject_level, 'retcam_random')
Confusion Matrix and Statistics

          Reference
Prediction  0  1
         0 24  2
         1 19 25
                                          
               Accuracy : 0.7             
                 95% CI : (0.5787, 0.8038)
    No Information Rate : 0.6143          
    P-Value [Acc > NIR] : 0.0869051       
                                          
                  Kappa : 0.4333          
                                          
 Mcnemar's Test P-Value : 0.0004803       
                                          
            Sensitivity : 0.9259          
            Specificity : 0.5581          
         Pos Pred Value : 0.5682          
         Neg Pred Value : 0.9231          
             Prevalence : 0.3857          
         Detection Rate : 0.3571          
   Detection Prevalence : 0.6286          
      Balanced Accuracy : 0.7420          
                                          
       'Positive' Class : 1               
                                          

Segmentations: zero all pixels < 0

compute_aupr(subject_level$filter_0, subject_level$label, 'PR: Segmentations: zero all pixels < 0')

compute_auroc(subject_level$filter_0, subject_level$label, 'ROC: Segmentations: zero all pixels < 0')

confusion_matrix(subject_level, 'filter_0')
Confusion Matrix and Statistics

          Reference
Prediction  0  1
         0 42  2
         1  1 25
                                          
               Accuracy : 0.9571          
                 95% CI : (0.8798, 0.9911)
    No Information Rate : 0.6143          
    P-Value [Acc > NIR] : 2.232e-11       
                                          
                  Kappa : 0.9089          
                                          
 Mcnemar's Test P-Value : 1               
                                          
            Sensitivity : 0.9259          
            Specificity : 0.9767          
         Pos Pred Value : 0.9615          
         Neg Pred Value : 0.9545          
             Prevalence : 0.3857          
         Detection Rate : 0.3571          
   Detection Prevalence : 0.3714          
      Balanced Accuracy : 0.9513          
                                          
       'Positive' Class : 1               
                                          
compute_aupr(subject_level$filter_0_binarized, subject_level$label, 'PR: Segmentations: zero all pixels < 0 and binarize')

compute_auroc(subject_level$filter_0_binarized, subject_level$label, 'ROC: Segmentations: zero all pixels < 0 and binarize')

confusion_matrix(subject_level, 'filter_0_binarized')
Confusion Matrix and Statistics

          Reference
Prediction  0  1
         0 41  0
         1  2 27
                                          
               Accuracy : 0.9714          
                 95% CI : (0.9006, 0.9965)
    No Information Rate : 0.6143          
    P-Value [Acc > NIR] : 1.53e-12        
                                          
                  Kappa : 0.9405          
                                          
 Mcnemar's Test P-Value : 0.4795          
                                          
            Sensitivity : 1.0000          
            Specificity : 0.9535          
         Pos Pred Value : 0.9310          
         Neg Pred Value : 1.0000          
             Prevalence : 0.3857          
         Detection Rate : 0.3857          
   Detection Prevalence : 0.4143          
      Balanced Accuracy : 0.9767          
                                          
       'Positive' Class : 1               
                                          
compute_aupr(subject_level$filter_0_skeletonized, subject_level$label, 'PR: Segmentations: zero all pixels < 0 and skeletonize')

compute_auroc(subject_level$filter_0_skeletonized, subject_level$label, 'ROC: Segmentations: zero all pixels < 0 and skeletonize')

confusion_matrix(subject_level, 'filter_0_skeletonized')
Confusion Matrix and Statistics

          Reference
Prediction  0  1
         0 40  0
         1  3 27
                                          
               Accuracy : 0.9571          
                 95% CI : (0.8798, 0.9911)
    No Information Rate : 0.6143          
    P-Value [Acc > NIR] : 2.232e-11       
                                          
                  Kappa : 0.9114          
                                          
 Mcnemar's Test P-Value : 0.2482          
                                          
            Sensitivity : 1.0000          
            Specificity : 0.9302          
         Pos Pred Value : 0.9000          
         Neg Pred Value : 1.0000          
             Prevalence : 0.3857          
         Detection Rate : 0.3857          
   Detection Prevalence : 0.4286          
      Balanced Accuracy : 0.9651          
                                          
       'Positive' Class : 1               
                                          
compute_aupr(subject_level$filter_0_random, subject_level$label, 'PR: Segmentations: zero all pixels < 0 - Shuffled Labels')

compute_auroc(subject_level$filter_0_random, subject_level$label, 'ROC: Segmentations: zero all pixels < 0 - Shuffled Labels')

confusion_matrix(subject_level, 'filter_0_random')
Warning in confusionMatrix.default(preds, df$label, positive = "1") :
  Levels are not in the same order for reference and data. Refactoring data to match.
Confusion Matrix and Statistics

          Reference
Prediction  0  1
         0 43 27
         1  0  0
                                          
               Accuracy : 0.6143          
                 95% CI : (0.4903, 0.7283)
    No Information Rate : 0.6143          
    P-Value [Acc > NIR] : 0.5525          
                                          
                  Kappa : 0               
                                          
 Mcnemar's Test P-Value : 5.624e-07       
                                          
            Sensitivity : 0.0000          
            Specificity : 1.0000          
         Pos Pred Value :    NaN          
         Neg Pred Value : 0.6143          
             Prevalence : 0.3857          
         Detection Rate : 0.0000          
   Detection Prevalence : 0.0000          
      Balanced Accuracy : 0.5000          
                                          
       'Positive' Class : 1               
                                          

Segmentations: zero all pixels < 50

compute_aupr(subject_level$filter_50, subject_level$label, 'PR: Segmentations: zero all pixels < 50')

compute_auroc(subject_level$filter_50, subject_level$label, 'ROC: Segmentations: zero all pixels < 50')

confusion_matrix(subject_level, 'filter_50')
Confusion Matrix and Statistics

          Reference
Prediction  0  1
         0 36  0
         1  7 27
                                          
               Accuracy : 0.9             
                 95% CI : (0.8048, 0.9588)
    No Information Rate : 0.6143          
    P-Value [Acc > NIR] : 8.517e-08       
                                          
                  Kappa : 0.7987          
                                          
 Mcnemar's Test P-Value : 0.02334         
                                          
            Sensitivity : 1.0000          
            Specificity : 0.8372          
         Pos Pred Value : 0.7941          
         Neg Pred Value : 1.0000          
             Prevalence : 0.3857          
         Detection Rate : 0.3857          
   Detection Prevalence : 0.4857          
      Balanced Accuracy : 0.9186          
                                          
       'Positive' Class : 1               
                                          
compute_aupr(subject_level$filter_50_binarized, subject_level$label, 'PR: Segmentations: zero all pixels < 50 and binarize')

compute_auroc(subject_level$filter_50_binarized, subject_level$label, 'ROC: Segmentations: zero all pixels < 50 and binarize')

confusion_matrix(subject_level, 'filter_0_binarized')
Confusion Matrix and Statistics

          Reference
Prediction  0  1
         0 41  0
         1  2 27
                                          
               Accuracy : 0.9714          
                 95% CI : (0.9006, 0.9965)
    No Information Rate : 0.6143          
    P-Value [Acc > NIR] : 1.53e-12        
                                          
                  Kappa : 0.9405          
                                          
 Mcnemar's Test P-Value : 0.4795          
                                          
            Sensitivity : 1.0000          
            Specificity : 0.9535          
         Pos Pred Value : 0.9310          
         Neg Pred Value : 1.0000          
             Prevalence : 0.3857          
         Detection Rate : 0.3857          
   Detection Prevalence : 0.4143          
      Balanced Accuracy : 0.9767          
                                          
       'Positive' Class : 1               
                                          
compute_aupr(subject_level$filter_50_skeletonized, subject_level$label, 'PR: Segmentations: zero all pixels < 50 and skeletonize')

compute_auroc(subject_level$filter_50_skeletonized, subject_level$label, 'ROC: Segmentations: zero all pixels < 50 and skeletonize')

confusion_matrix(subject_level, 'filter_50_skeletonized')
Confusion Matrix and Statistics

          Reference
Prediction  0  1
         0 19  0
         1 24 27
                                         
               Accuracy : 0.6571         
                 95% CI : (0.534, 0.7665)
    No Information Rate : 0.6143         
    P-Value [Acc > NIR] : 0.2718         
                                         
                  Kappa : 0.3792         
                                         
 Mcnemar's Test P-Value : 2.668e-06      
                                         
            Sensitivity : 1.0000         
            Specificity : 0.4419         
         Pos Pred Value : 0.5294         
         Neg Pred Value : 1.0000         
             Prevalence : 0.3857         
         Detection Rate : 0.3857         
   Detection Prevalence : 0.7286         
      Balanced Accuracy : 0.7209         
                                         
       'Positive' Class : 1              
                                         

Segmentations: zero all pixels < 100

compute_aupr(subject_level$filter_100, subject_level$label, 'PR: Segmentations: zero all pixels < 100')

compute_auroc(subject_level$filter_100, subject_level$label, 'ROC: Segmentations: zero all pixels < 100')

confusion_matrix(subject_level, 'filter_100')
Confusion Matrix and Statistics

          Reference
Prediction  0  1
         0 43 10
         1  0 17
                                          
               Accuracy : 0.8571          
                 95% CI : (0.7529, 0.9293)
    No Information Rate : 0.6143          
    P-Value [Acc > NIR] : 7.748e-06       
                                          
                  Kappa : 0.6762          
                                          
 Mcnemar's Test P-Value : 0.004427        
                                          
            Sensitivity : 0.6296          
            Specificity : 1.0000          
         Pos Pred Value : 1.0000          
         Neg Pred Value : 0.8113          
             Prevalence : 0.3857          
         Detection Rate : 0.2429          
   Detection Prevalence : 0.2429          
      Balanced Accuracy : 0.8148          
                                          
       'Positive' Class : 1               
                                          
compute_aupr(subject_level$filter_100_binarized, subject_level$label, 'PR: Segmentations: zero all pixels < 100 and binarize')

compute_auroc(subject_level$filter_100_binarized, subject_level$label, 'ROC: Segmentations: zero all pixels < 100 and binarize')

confusion_matrix(subject_level, 'filter_100_binarized')
Confusion Matrix and Statistics

          Reference
Prediction  0  1
         0 39  4
         1  4 23
                                          
               Accuracy : 0.8857          
                 95% CI : (0.7872, 0.9493)
    No Information Rate : 0.6143          
    P-Value [Acc > NIR] : 4.352e-07       
                                          
                  Kappa : 0.7588          
                                          
 Mcnemar's Test P-Value : 1               
                                          
            Sensitivity : 0.8519          
            Specificity : 0.9070          
         Pos Pred Value : 0.8519          
         Neg Pred Value : 0.9070          
             Prevalence : 0.3857          
         Detection Rate : 0.3286          
   Detection Prevalence : 0.3857          
      Balanced Accuracy : 0.8794          
                                          
       'Positive' Class : 1               
                                          
compute_aupr(subject_level$filter_100_skeletonized, subject_level$label, 'PR: Segmentations: zero all pixels < 100 and skeletonize')

compute_auroc(subject_level$filter_100_skeletonized, subject_level$label, 'ROC: Segmentations: zero all pixels < 100 and skeletonize')

confusion_matrix(subject_level, 'filter_100_skeletonized')
Confusion Matrix and Statistics

          Reference
Prediction  0  1
         0 38  3
         1  5 24
                                          
               Accuracy : 0.8857          
                 95% CI : (0.7872, 0.9493)
    No Information Rate : 0.6143          
    P-Value [Acc > NIR] : 4.352e-07       
                                          
                  Kappa : 0.7621          
                                          
 Mcnemar's Test P-Value : 0.7237          
                                          
            Sensitivity : 0.8889          
            Specificity : 0.8837          
         Pos Pred Value : 0.8276          
         Neg Pred Value : 0.9268          
             Prevalence : 0.3857          
         Detection Rate : 0.3429          
   Detection Prevalence : 0.4143          
      Balanced Accuracy : 0.8863          
                                          
       'Positive' Class : 1               
                                          

Segmentations: zero all pixels < 150

compute_aupr(subject_level$filter_150, subject_level$label, 'PR: Segmentations: zero all pixels < 150')

compute_auroc(subject_level$filter_150, subject_level$label, 'ROC: Segmentations: zero all pixels < 150')

confusion_matrix(subject_level, 'filter_150')
Confusion Matrix and Statistics

          Reference
Prediction  0  1
         0 37  0
         1  6 27
                                          
               Accuracy : 0.9143          
                 95% CI : (0.8227, 0.9679)
    No Information Rate : 0.6143          
    P-Value [Acc > NIR] : 1.438e-08       
                                          
                  Kappa : 0.8263          
                                          
 Mcnemar's Test P-Value : 0.04123         
                                          
            Sensitivity : 1.0000          
            Specificity : 0.8605          
         Pos Pred Value : 0.8182          
         Neg Pred Value : 1.0000          
             Prevalence : 0.3857          
         Detection Rate : 0.3857          
   Detection Prevalence : 0.4714          
      Balanced Accuracy : 0.9302          
                                          
       'Positive' Class : 1               
                                          
compute_aupr(subject_level$filter_150_binarized, subject_level$label, 'PR: Segmentations: zero all pixels < 150 and binarize')

compute_auroc(subject_level$filter_150_binarized, subject_level$label, 'ROC: Segmentations: zero all pixels < 150 and binarize')

confusion_matrix(subject_level, 'filter_150_binarized')
Confusion Matrix and Statistics

          Reference
Prediction  0  1
         0 43  5
         1  0 22
                                          
               Accuracy : 0.9286          
                 95% CI : (0.8411, 0.9764)
    No Information Rate : 0.6143          
    P-Value [Acc > NIR] : 2.054e-09       
                                          
                  Kappa : 0.8439          
                                          
 Mcnemar's Test P-Value : 0.07364         
                                          
            Sensitivity : 0.8148          
            Specificity : 1.0000          
         Pos Pred Value : 1.0000          
         Neg Pred Value : 0.8958          
             Prevalence : 0.3857          
         Detection Rate : 0.3143          
   Detection Prevalence : 0.3143          
      Balanced Accuracy : 0.9074          
                                          
       'Positive' Class : 1               
                                          
compute_aupr(subject_level$filter_150_skeletonized, subject_level$label, 'PR: Segmentations: zero all pixels < 150 and skeletonize')

compute_auroc(subject_level$filter_150_skeletonized, subject_level$label, 'ROC: Segmentations: zero all pixels < 150 and skeletonize')

confusion_matrix(subject_level, 'filter_150_skeletonized')
Confusion Matrix and Statistics

          Reference
Prediction  0  1
         0 38  3
         1  5 24
                                          
               Accuracy : 0.8857          
                 95% CI : (0.7872, 0.9493)
    No Information Rate : 0.6143          
    P-Value [Acc > NIR] : 4.352e-07       
                                          
                  Kappa : 0.7621          
                                          
 Mcnemar's Test P-Value : 0.7237          
                                          
            Sensitivity : 0.8889          
            Specificity : 0.8837          
         Pos Pred Value : 0.8276          
         Neg Pred Value : 0.9268          
             Prevalence : 0.3857          
         Detection Rate : 0.3429          
   Detection Prevalence : 0.4143          
      Balanced Accuracy : 0.8863          
                                          
       'Positive' Class : 1               
                                          

Segmentations: zero all pixels < 200

compute_aupr(subject_level$filter_200, subject_level$label, 'PR: Segmentations: zero all pixels < 200')

compute_auroc(subject_level$filter_200, subject_level$label, 'ROC: Segmentations: zero all pixels < 200')

confusion_matrix(subject_level, 'filter_200')
Confusion Matrix and Statistics

          Reference
Prediction  0  1
         0 34  4
         1  9 23
                                          
               Accuracy : 0.8143          
                 95% CI : (0.7034, 0.8972)
    No Information Rate : 0.6143          
    P-Value [Acc > NIR] : 0.0002607       
                                          
                  Kappa : 0.6211          
                                          
 Mcnemar's Test P-Value : 0.2672575       
                                          
            Sensitivity : 0.8519          
            Specificity : 0.7907          
         Pos Pred Value : 0.7187          
         Neg Pred Value : 0.8947          
             Prevalence : 0.3857          
         Detection Rate : 0.3286          
   Detection Prevalence : 0.4571          
      Balanced Accuracy : 0.8213          
                                          
       'Positive' Class : 1               
                                          
compute_aupr(subject_level$filter_200_binarized, subject_level$label, 'PR: Segmentations: zero all pixels < 200 and binarize')

compute_auroc(subject_level$filter_200_binarized, subject_level$label, 'ROC: Segmentations: zero all pixels < 200 and binarize')

confusion_matrix(subject_level, 'filter_200_binarized')
Confusion Matrix and Statistics

          Reference
Prediction  0  1
         0 40 12
         1  3 15
                                          
               Accuracy : 0.7857          
                 95% CI : (0.6713, 0.8748)
    No Information Rate : 0.6143          
    P-Value [Acc > NIR] : 0.001729        
                                          
                  Kappa : 0.5179          
                                          
 Mcnemar's Test P-Value : 0.038867        
                                          
            Sensitivity : 0.5556          
            Specificity : 0.9302          
         Pos Pred Value : 0.8333          
         Neg Pred Value : 0.7692          
             Prevalence : 0.3857          
         Detection Rate : 0.2143          
   Detection Prevalence : 0.2571          
      Balanced Accuracy : 0.7429          
                                          
       'Positive' Class : 1               
                                          
compute_aupr(subject_level$filter_200_skeletonized, subject_level$label, 'PR: Segmentations: zero all pixels < 200 and skeletonize')

compute_auroc(subject_level$filter_200_skeletonized, subject_level$label, 'ROC: Segmentations: zero all pixels < 200 and skeletonize')

confusion_matrix(subject_level, 'filter_200_skeletonized')
Confusion Matrix and Statistics

          Reference
Prediction  0  1
         0 43 25
         1  0  2
                                          
               Accuracy : 0.6429          
                 95% CI : (0.5193, 0.7539)
    No Information Rate : 0.6143          
    P-Value [Acc > NIR] : 0.3595          
                                          
                  Kappa : 0.0895          
                                          
 Mcnemar's Test P-Value : 1.587e-06       
                                          
            Sensitivity : 0.07407         
            Specificity : 1.00000         
         Pos Pred Value : 1.00000         
         Neg Pred Value : 0.63235         
             Prevalence : 0.38571         
         Detection Rate : 0.02857         
   Detection Prevalence : 0.02857         
      Balanced Accuracy : 0.53704         
                                          
       'Positive' Class : 1               
                                          

Segmentations: zero all pixels < 210

compute_aupr(subject_level$filter_210, subject_level$label, 'PR: Segmentations: zero all pixels < 210')

compute_auroc(subject_level$filter_210, subject_level$label, 'ROC: Segmentations: zero all pixels < 210')

confusion_matrix(subject_level, 'filter_210')
Confusion Matrix and Statistics

          Reference
Prediction  0  1
         0 43 20
         1  0  7
                                         
               Accuracy : 0.7143         
                 95% CI : (0.5938, 0.816)
    No Information Rate : 0.6143         
    P-Value [Acc > NIR] : 0.0532         
                                         
                  Kappa : 0.3007         
                                         
 Mcnemar's Test P-Value : 2.152e-05      
                                         
            Sensitivity : 0.2593         
            Specificity : 1.0000         
         Pos Pred Value : 1.0000         
         Neg Pred Value : 0.6825         
             Prevalence : 0.3857         
         Detection Rate : 0.1000         
   Detection Prevalence : 0.1000         
      Balanced Accuracy : 0.6296         
                                         
       'Positive' Class : 1              
                                         
compute_aupr(subject_level$filter_210_binarized, subject_level$label, 'PR: Segmentations: zero all pixels < 210 and binarize')

compute_auroc(subject_level$filter_210_binarized, subject_level$label, 'ROC: Segmentations: zero all pixels < 210 and binarize')

confusion_matrix(subject_level, 'filter_210_binarized')
Confusion Matrix and Statistics

          Reference
Prediction  0  1
         0 39  9
         1  4 18
                                          
               Accuracy : 0.8143          
                 95% CI : (0.7034, 0.8972)
    No Information Rate : 0.6143          
    P-Value [Acc > NIR] : 0.0002607       
                                          
                  Kappa : 0.5941          
                                          
 Mcnemar's Test P-Value : 0.2672575       
                                          
            Sensitivity : 0.6667          
            Specificity : 0.9070          
         Pos Pred Value : 0.8182          
         Neg Pred Value : 0.8125          
             Prevalence : 0.3857          
         Detection Rate : 0.2571          
   Detection Prevalence : 0.3143          
      Balanced Accuracy : 0.7868          
                                          
       'Positive' Class : 1               
                                          
compute_aupr(subject_level$filter_210_skeletonized, subject_level$label, 'PR: Segmentations: zero all pixels < 210 and skeletonize')

compute_auroc(subject_level$filter_210_skeletonized, subject_level$label, 'ROC: Segmentations: zero all pixels < 210 and skeletonize')

confusion_matrix(subject_level, 'filter_210_skeletonized')
Confusion Matrix and Statistics

          Reference
Prediction  0  1
         0 35  7
         1  8 20
                                          
               Accuracy : 0.7857          
                 95% CI : (0.6713, 0.8748)
    No Information Rate : 0.6143          
    P-Value [Acc > NIR] : 0.001729        
                                          
                  Kappa : 0.5509          
                                          
 Mcnemar's Test P-Value : 1.000000        
                                          
            Sensitivity : 0.7407          
            Specificity : 0.8140          
         Pos Pred Value : 0.7143          
         Neg Pred Value : 0.8333          
             Prevalence : 0.3857          
         Detection Rate : 0.2857          
   Detection Prevalence : 0.4000          
      Balanced Accuracy : 0.7773          
                                          
       'Positive' Class : 1               
                                          

Segmentations: zero all pixels < 220

compute_aupr(subject_level$filter_220, subject_level$label, 'PR: Segmentations: zero all pixels < 220')

compute_auroc(subject_level$filter_220, subject_level$label, 'ROC: Segmentations: zero all pixels < 220')

confusion_matrix(subject_level, 'filter_220')
Confusion Matrix and Statistics

          Reference
Prediction  0  1
         0 34 12
         1  9 15
                                          
               Accuracy : 0.7             
                 95% CI : (0.5787, 0.8038)
    No Information Rate : 0.6143          
    P-Value [Acc > NIR] : 0.08691         
                                          
                  Kappa : 0.3536          
                                          
 Mcnemar's Test P-Value : 0.66252         
                                          
            Sensitivity : 0.5556          
            Specificity : 0.7907          
         Pos Pred Value : 0.6250          
         Neg Pred Value : 0.7391          
             Prevalence : 0.3857          
         Detection Rate : 0.2143          
   Detection Prevalence : 0.3429          
      Balanced Accuracy : 0.6731          
                                          
       'Positive' Class : 1               
                                          
compute_aupr(subject_level$filter_220_binarized, subject_level$label, 'PR: Segmentations: zero all pixels < 220 and binarize')

compute_auroc(subject_level$filter_220_binarized, subject_level$label, 'ROC: Segmentations: zero all pixels < 220 and binarize')

confusion_matrix(subject_level, 'filter_220_binarized')
Confusion Matrix and Statistics

          Reference
Prediction  0  1
         0 31  7
         1 12 20
                                        
               Accuracy : 0.7286        
                 95% CI : (0.609, 0.828)
    No Information Rate : 0.6143        
    P-Value [Acc > NIR] : 0.03066       
                                        
                  Kappa : 0.4463        
                                        
 Mcnemar's Test P-Value : 0.35880       
                                        
            Sensitivity : 0.7407        
            Specificity : 0.7209        
         Pos Pred Value : 0.6250        
         Neg Pred Value : 0.8158        
             Prevalence : 0.3857        
         Detection Rate : 0.2857        
   Detection Prevalence : 0.4571        
      Balanced Accuracy : 0.7308        
                                        
       'Positive' Class : 1             
                                        
compute_aupr(subject_level$filter_220_skeletonized, subject_level$label, 'PR: Segmentations: zero all pixels < 220 and skeletonize')

compute_auroc(subject_level$filter_220_skeletonized, subject_level$label, 'ROC: Segmentations: zero all pixels < 220 and skeletonize')

confusion_matrix(subject_level, 'filter_220_skeletonized')
Warning in confusionMatrix.default(preds, df$label, positive = "1") :
  Levels are not in the same order for reference and data. Refactoring data to match.
Confusion Matrix and Statistics

          Reference
Prediction  0  1
         0 43 27
         1  0  0
                                          
               Accuracy : 0.6143          
                 95% CI : (0.4903, 0.7283)
    No Information Rate : 0.6143          
    P-Value [Acc > NIR] : 0.5525          
                                          
                  Kappa : 0               
                                          
 Mcnemar's Test P-Value : 5.624e-07       
                                          
            Sensitivity : 0.0000          
            Specificity : 1.0000          
         Pos Pred Value :    NaN          
         Neg Pred Value : 0.6143          
             Prevalence : 0.3857          
         Detection Rate : 0.0000          
   Detection Prevalence : 0.0000          
      Balanced Accuracy : 0.5000          
                                          
       'Positive' Class : 1               
                                          

Segmentations: zero all pixels < 230

compute_aupr(subject_level$filter_230, subject_level$label, 'PR: Segmentations: zero all pixels < 230')

compute_auroc(subject_level$filter_230, subject_level$label, 'ROC: Segmentations: zero all pixels < 230')

confusion_matrix(subject_level, 'filter_230')
Confusion Matrix and Statistics

          Reference
Prediction  0  1
         0 26  6
         1 17 21
                                          
               Accuracy : 0.6714          
                 95% CI : (0.5488, 0.7791)
    No Information Rate : 0.6143          
    P-Value [Acc > NIR] : 0.19581         
                                          
                  Kappa : 0.3555          
                                          
 Mcnemar's Test P-Value : 0.03706         
                                          
            Sensitivity : 0.7778          
            Specificity : 0.6047          
         Pos Pred Value : 0.5526          
         Neg Pred Value : 0.8125          
             Prevalence : 0.3857          
         Detection Rate : 0.3000          
   Detection Prevalence : 0.5429          
      Balanced Accuracy : 0.6912          
                                          
       'Positive' Class : 1               
                                          
compute_aupr(subject_level$filter_230_binarized, subject_level$label, 'PR: Segmentations: zero all pixels < 230 and binarize')

compute_auroc(subject_level$filter_230_binarized, subject_level$label, 'ROC: Segmentations: zero all pixels < 230 and binarize')

confusion_matrix(subject_level, 'filter_230_binarized')
Confusion Matrix and Statistics

          Reference
Prediction  0  1
         0 10  1
         1 33 26
                                          
               Accuracy : 0.5143          
                 95% CI : (0.3917, 0.6356)
    No Information Rate : 0.6143          
    P-Value [Acc > NIR] : 0.966           
                                          
                  Kappa : 0.1602          
                                          
 Mcnemar's Test P-Value : 1.058e-07       
                                          
            Sensitivity : 0.9630          
            Specificity : 0.2326          
         Pos Pred Value : 0.4407          
         Neg Pred Value : 0.9091          
             Prevalence : 0.3857          
         Detection Rate : 0.3714          
   Detection Prevalence : 0.8429          
      Balanced Accuracy : 0.5978          
                                          
       'Positive' Class : 1               
                                          
compute_aupr(subject_level$filter_230_skeletonized, subject_level$label, 'PR: Segmentations: zero all pixels < 230 and skeletonize')

compute_auroc(subject_level$filter_230_skeletonized, subject_level$label, 'ROC: Segmentations: zero all pixels < 230 and skeletonize')

confusion_matrix(subject_level, 'filter_230_skeletonized')
Confusion Matrix and Statistics

          Reference
Prediction  0  1
         0 17  2
         1 26 25
                                          
               Accuracy : 0.6             
                 95% CI : (0.4759, 0.7153)
    No Information Rate : 0.6143          
    P-Value [Acc > NIR] : 0.6466          
                                          
                  Kappa : 0.2757          
                                          
 Mcnemar's Test P-Value : 1.383e-05       
                                          
            Sensitivity : 0.9259          
            Specificity : 0.3953          
         Pos Pred Value : 0.4902          
         Neg Pred Value : 0.8947          
             Prevalence : 0.3857          
         Detection Rate : 0.3571          
   Detection Prevalence : 0.7286          
      Balanced Accuracy : 0.6606          
                                          
       'Positive' Class : 1               
                                          

Segmentations: zero all pixels < 240

compute_aupr(subject_level$filter_240, subject_level$label, 'PR: Segmentations: zero all pixels < 240')

compute_auroc(subject_level$filter_240, subject_level$label, 'ROC: Segmentations: zero all pixels < 240')

confusion_matrix(subject_level, 'filter_240')
Confusion Matrix and Statistics

          Reference
Prediction  0  1
         0 23  9
         1 20 18
                                          
               Accuracy : 0.5857          
                 95% CI : (0.4617, 0.7023)
    No Information Rate : 0.6143          
    P-Value [Acc > NIR] : 0.73214         
                                          
                  Kappa : 0.1873          
                                          
 Mcnemar's Test P-Value : 0.06332         
                                          
            Sensitivity : 0.6667          
            Specificity : 0.5349          
         Pos Pred Value : 0.4737          
         Neg Pred Value : 0.7187          
             Prevalence : 0.3857          
         Detection Rate : 0.2571          
   Detection Prevalence : 0.5429          
      Balanced Accuracy : 0.6008          
                                          
       'Positive' Class : 1               
                                          
compute_aupr(subject_level$filter_240_binarized, subject_level$label, 'PR: Segmentations: zero all pixels < 240 and binarize')

compute_auroc(subject_level$filter_240_binarized, subject_level$label, 'ROC: Segmentations: zero all pixels < 240 and binarize')

confusion_matrix(subject_level, 'filter_240_binarized')
Confusion Matrix and Statistics

          Reference
Prediction  0  1
         0 12  1
         1 31 26
                                          
               Accuracy : 0.5429          
                 95% CI : (0.4194, 0.6626)
    No Information Rate : 0.6143          
    P-Value [Acc > NIR] : 0.9106          
                                          
                  Kappa : 0.2006          
                                          
 Mcnemar's Test P-Value : 2.951e-07       
                                          
            Sensitivity : 0.9630          
            Specificity : 0.2791          
         Pos Pred Value : 0.4561          
         Neg Pred Value : 0.9231          
             Prevalence : 0.3857          
         Detection Rate : 0.3714          
   Detection Prevalence : 0.8143          
      Balanced Accuracy : 0.6210          
                                          
       'Positive' Class : 1               
                                          
compute_aupr(subject_level$filter_240_skeletonized, subject_level$label, 'PR: Segmentations: zero all pixels < 240 and skeletonize')

compute_auroc(subject_level$filter_240_skeletonized, subject_level$label, 'ROC: Segmentations: zero all pixels < 240 and skeletonize')

confusion_matrix(subject_level, 'filter_240_skeletonized')
Confusion Matrix and Statistics

          Reference
Prediction  0  1
         0 14  2
         1 29 25
                                          
               Accuracy : 0.5571          
                 95% CI : (0.4334, 0.6759)
    No Information Rate : 0.6143          
    P-Value [Acc > NIR] : 0.8651          
                                          
                  Kappa : 0.2121          
                                          
 Mcnemar's Test P-Value : 3.016e-06       
                                          
            Sensitivity : 0.9259          
            Specificity : 0.3256          
         Pos Pred Value : 0.4630          
         Neg Pred Value : 0.8750          
             Prevalence : 0.3857          
         Detection Rate : 0.3571          
   Detection Prevalence : 0.7714          
      Balanced Accuracy : 0.6258          
                                          
       'Positive' Class : 1               
                                          

Segmentations: zero all pixels < 250

compute_aupr(subject_level$filter_250, subject_level$label, 'PR: Segmentations: zero all pixels < 250')

compute_auroc(subject_level$filter_250, subject_level$label, 'ROC: Segmentations: zero all pixels < 250')

confusion_matrix(subject_level, 'filter_250')
Confusion Matrix and Statistics

          Reference
Prediction  0  1
         0  1  0
         1 42 27
                                          
               Accuracy : 0.4             
                 95% CI : (0.2847, 0.5241)
    No Information Rate : 0.6143          
    P-Value [Acc > NIR] : 0.9999          
                                          
                  Kappa : 0.018           
                                          
 Mcnemar's Test P-Value : 2.509e-10       
                                          
            Sensitivity : 1.00000         
            Specificity : 0.02326         
         Pos Pred Value : 0.39130         
         Neg Pred Value : 1.00000         
             Prevalence : 0.38571         
         Detection Rate : 0.38571         
   Detection Prevalence : 0.98571         
      Balanced Accuracy : 0.51163         
                                          
       'Positive' Class : 1               
                                          
compute_aupr(subject_level$filter_250_binarized, subject_level$label, 'PR: Segmentations: zero all pixels < 250 and binarize')

compute_auroc(subject_level$filter_250_binarized, subject_level$label, 'ROC: Segmentations: zero all pixels < 250 and binarize')

confusion_matrix(subject_level, 'filter_250_binarized')
Confusion Matrix and Statistics

          Reference
Prediction  0  1
         0  1  0
         1 42 27
                                          
               Accuracy : 0.4             
                 95% CI : (0.2847, 0.5241)
    No Information Rate : 0.6143          
    P-Value [Acc > NIR] : 0.9999          
                                          
                  Kappa : 0.018           
                                          
 Mcnemar's Test P-Value : 2.509e-10       
                                          
            Sensitivity : 1.00000         
            Specificity : 0.02326         
         Pos Pred Value : 0.39130         
         Neg Pred Value : 1.00000         
             Prevalence : 0.38571         
         Detection Rate : 0.38571         
   Detection Prevalence : 0.98571         
      Balanced Accuracy : 0.51163         
                                          
       'Positive' Class : 1               
                                          
compute_aupr(subject_level$filter_250_skeletonized, subject_level$label, 'PR: Segmentations: zero all pixels < 250 and skeletonize')

compute_auroc(subject_level$filter_250_skeletonized, subject_level$label, 'ROC: Segmentations: zero all pixels < 250 and skeletonize')

confusion_matrix(subject_level, 'filter_250_skeletonized')
Confusion Matrix and Statistics

          Reference
Prediction  0  1
         0  1  0
         1 42 27
                                          
               Accuracy : 0.4             
                 95% CI : (0.2847, 0.5241)
    No Information Rate : 0.6143          
    P-Value [Acc > NIR] : 0.9999          
                                          
                  Kappa : 0.018           
                                          
 Mcnemar's Test P-Value : 2.509e-10       
                                          
            Sensitivity : 1.00000         
            Specificity : 0.02326         
         Pos Pred Value : 0.39130         
         Neg Pred Value : 1.00000         
             Prevalence : 0.38571         
         Detection Rate : 0.38571         
   Detection Prevalence : 0.98571         
      Balanced Accuracy : 0.51163         
                                          
       'Positive' Class : 1               
                                          

Segmentations: zero all pixels < 257

compute_aupr(subject_level$filter_257, subject_level$label, 'PR: Segmentations: zero all pixels < 257')

compute_auroc(subject_level$filter_257, subject_level$label, 'ROC: Segmentations: zero all pixels < 257')

confusion_matrix(subject_level, 'filter_257')
Warning in confusionMatrix.default(preds, df$label, positive = "1") :
  Levels are not in the same order for reference and data. Refactoring data to match.
Confusion Matrix and Statistics

          Reference
Prediction  0  1
         0 43 27
         1  0  0
                                          
               Accuracy : 0.6143          
                 95% CI : (0.4903, 0.7283)
    No Information Rate : 0.6143          
    P-Value [Acc > NIR] : 0.5525          
                                          
                  Kappa : 0               
                                          
 Mcnemar's Test P-Value : 5.624e-07       
                                          
            Sensitivity : 0.0000          
            Specificity : 1.0000          
         Pos Pred Value :    NaN          
         Neg Pred Value : 0.6143          
             Prevalence : 0.3857          
         Detection Rate : 0.0000          
   Detection Prevalence : 0.0000          
      Balanced Accuracy : 0.5000          
                                          
       'Positive' Class : 1               
                                          
compute_aupr(subject_level$filter_257_binarized, subject_level$label, 'PR: Segmentations: zero all pixels < 257 and binarize')

compute_auroc(subject_level$filter_257_binarized, subject_level$label, 'ROC: Segmentations: zero all pixels < 257 and binarize')

confusion_matrix(subject_level, 'filter_257_binarized')
Warning in confusionMatrix.default(preds, df$label, positive = "1") :
  Levels are not in the same order for reference and data. Refactoring data to match.
Confusion Matrix and Statistics

          Reference
Prediction  0  1
         0 43 27
         1  0  0
                                          
               Accuracy : 0.6143          
                 95% CI : (0.4903, 0.7283)
    No Information Rate : 0.6143          
    P-Value [Acc > NIR] : 0.5525          
                                          
                  Kappa : 0               
                                          
 Mcnemar's Test P-Value : 5.624e-07       
                                          
            Sensitivity : 0.0000          
            Specificity : 1.0000          
         Pos Pred Value :    NaN          
         Neg Pred Value : 0.6143          
             Prevalence : 0.3857          
         Detection Rate : 0.0000          
   Detection Prevalence : 0.0000          
      Balanced Accuracy : 0.5000          
                                          
       'Positive' Class : 1               
                                          
compute_aupr(subject_level$filter_257_skeletonized, subject_level$label, 'PR: Segmentations: zero all pixels < 257 and skeletonize')

compute_auroc(subject_level$filter_257_skeletonized, subject_level$label, 'ROC: Segmentations: zero all pixels < 257 and skeletonize')

confusion_matrix(subject_level, 'filter_257_skeletonized')
Warning in confusionMatrix.default(preds, df$label, positive = "1") :
  Levels are not in the same order for reference and data. Refactoring data to match.
Confusion Matrix and Statistics

          Reference
Prediction  0  1
         0 43 27
         1  0  0
                                          
               Accuracy : 0.6143          
                 95% CI : (0.4903, 0.7283)
    No Information Rate : 0.6143          
    P-Value [Acc > NIR] : 0.5525          
                                          
                  Kappa : 0               
                                          
 Mcnemar's Test P-Value : 5.624e-07       
                                          
            Sensitivity : 0.0000          
            Specificity : 1.0000          
         Pos Pred Value :    NaN          
         Neg Pred Value : 0.6143          
             Prevalence : 0.3857          
         Detection Rate : 0.0000          
   Detection Prevalence : 0.0000          
      Balanced Accuracy : 0.5000          
                                          
       'Positive' Class : 1               
                                          

Segmentations: zero all pixels > 10

compute_aupr(subject_level$filter_10, subject_level$label, 'PR: Segmentations: zero all pixels < 10')

compute_auroc(subject_level$filter_10, subject_level$label, 'ROC: Segmentations: zero all pixels < 10')

confusion_matrix(subject_level, 'filter_10')
Confusion Matrix and Statistics

          Reference
Prediction  0  1
         0 41  8
         1  2 19
                                          
               Accuracy : 0.8571          
                 95% CI : (0.7529, 0.9293)
    No Information Rate : 0.6143          
    P-Value [Acc > NIR] : 7.748e-06       
                                          
                  Kappa : 0.6855          
                                          
 Mcnemar's Test P-Value : 0.1138          
                                          
            Sensitivity : 0.7037          
            Specificity : 0.9535          
         Pos Pred Value : 0.9048          
         Neg Pred Value : 0.8367          
             Prevalence : 0.3857          
         Detection Rate : 0.2714          
   Detection Prevalence : 0.3000          
      Balanced Accuracy : 0.8286          
                                          
       'Positive' Class : 1               
                                          
compute_aupr(subject_level$filter_10_binarized, subject_level$label, 'PR: Segmentations: zero all pixels < 10 and binarize')

compute_auroc(subject_level$filter_10_binarized, subject_level$label, 'ROC: Segmentations: zero all pixels < 10 and binarize')

confusion_matrix(subject_level, 'filter_10_binarized')
Confusion Matrix and Statistics

          Reference
Prediction  0  1
         0 34  0
         1  9 27
                                          
               Accuracy : 0.8714          
                 95% CI : (0.7699, 0.9395)
    No Information Rate : 0.6143          
    P-Value [Acc > NIR] : 1.949e-06       
                                          
                  Kappa : 0.7445          
                                          
 Mcnemar's Test P-Value : 0.007661        
                                          
            Sensitivity : 1.0000          
            Specificity : 0.7907          
         Pos Pred Value : 0.7500          
         Neg Pred Value : 1.0000          
             Prevalence : 0.3857          
         Detection Rate : 0.3857          
   Detection Prevalence : 0.5143          
      Balanced Accuracy : 0.8953          
                                          
       'Positive' Class : 1               
                                          
compute_aupr(subject_level$filter_10_skeletonized, subject_level$label, 'PR: Segmentations: zero all pixels < 10 and skeletonize')

compute_auroc(subject_level$filter_10_skeletonized, subject_level$label, 'ROC: Segmentations: zero all pixels < 10 and skeletonize')

confusion_matrix(subject_level, 'filter_10_skeletonized')
Confusion Matrix and Statistics

          Reference
Prediction  0  1
         0 39  0
         1  4 27
                                          
               Accuracy : 0.9429          
                 95% CI : (0.8601, 0.9842)
    No Information Rate : 0.6143          
    P-Value [Acc > NIR] : 2.41e-10        
                                          
                  Kappa : 0.8826          
                                          
 Mcnemar's Test P-Value : 0.1336          
                                          
            Sensitivity : 1.0000          
            Specificity : 0.9070          
         Pos Pred Value : 0.8710          
         Neg Pred Value : 1.0000          
             Prevalence : 0.3857          
         Detection Rate : 0.3857          
   Detection Prevalence : 0.4429          
      Balanced Accuracy : 0.9535          
                                          
       'Positive' Class : 1               
                                          

Segmentations: zero all pixels < 75 and > 150

compute_aupr(subject_level$filter_75, subject_level$label, 'PR: Segmentations: zero all pixels < 75 and > 150')

compute_auroc(subject_level$filter_75, subject_level$label, 'ROC: Segmentations: zero all pixels < 75 and > 150')

confusion_matrix(subject_level, 'filter_75')
Confusion Matrix and Statistics

          Reference
Prediction  0  1
         0 36  1
         1  7 26
                                          
               Accuracy : 0.8857          
                 95% CI : (0.7872, 0.9493)
    No Information Rate : 0.6143          
    P-Value [Acc > NIR] : 4.352e-07       
                                          
                  Kappa : 0.7684          
                                          
 Mcnemar's Test P-Value : 0.0771          
                                          
            Sensitivity : 0.9630          
            Specificity : 0.8372          
         Pos Pred Value : 0.7879          
         Neg Pred Value : 0.9730          
             Prevalence : 0.3857          
         Detection Rate : 0.3714          
   Detection Prevalence : 0.4714          
      Balanced Accuracy : 0.9001          
                                          
       'Positive' Class : 1               
                                          
compute_aupr(subject_level$filter_75_binarized, subject_level$label, 'PR: Segmentations: zero all pixels < 75 and > 150 and binarize')

compute_auroc(subject_level$filter_75_binarized, subject_level$label, 'ROC: Segmentations: zero all pixels < 75 and > 150 and binarize')

confusion_matrix(subject_level, 'filter_75_binarized')
Confusion Matrix and Statistics

          Reference
Prediction  0  1
         0 33  0
         1 10 27
                                          
               Accuracy : 0.8571          
                 95% CI : (0.7529, 0.9293)
    No Information Rate : 0.6143          
    P-Value [Acc > NIR] : 7.748e-06       
                                          
                  Kappa : 0.718           
                                          
 Mcnemar's Test P-Value : 0.004427        
                                          
            Sensitivity : 1.0000          
            Specificity : 0.7674          
         Pos Pred Value : 0.7297          
         Neg Pred Value : 1.0000          
             Prevalence : 0.3857          
         Detection Rate : 0.3857          
   Detection Prevalence : 0.5286          
      Balanced Accuracy : 0.8837          
                                          
       'Positive' Class : 1               
                                          
compute_aupr(subject_level$filter_75_skeletonized, subject_level$label, 'PR: Segmentations: zero all pixels < 75 and > 150 and skeletonize')

compute_auroc(subject_level$filter_75_skeletonized, subject_level$label, 'ROC: Segmentations: zero all pixels < 75 and > 150 and skeletonize')

confusion_matrix(subject_level, 'filter_75_skeletonized')
Confusion Matrix and Statistics

          Reference
Prediction  0  1
         0 43 13
         1  0 14
                                          
               Accuracy : 0.8143          
                 95% CI : (0.7034, 0.8972)
    No Information Rate : 0.6143          
    P-Value [Acc > NIR] : 0.0002607       
                                          
                  Kappa : 0.5695          
                                          
 Mcnemar's Test P-Value : 0.0008741       
                                          
            Sensitivity : 0.5185          
            Specificity : 1.0000          
         Pos Pred Value : 1.0000          
         Neg Pred Value : 0.7679          
             Prevalence : 0.3857          
         Detection Rate : 0.2000          
   Detection Prevalence : 0.2000          
      Balanced Accuracy : 0.7593          
                                          
       'Positive' Class : 1               
                                          
aupr_figures <- function(predictions, labels, title, names, save_name) {
    n_black <- length(labels[labels == 1])
    n_total <- length(labels)
    for (i in seq_along(predictions)) {
        preds <- prediction(predictions[i], labels)
        aupr <- performance(preds, measure = 'aucpr')
        aupr <- aupr@y.values[[1]]
        perf <- performance(preds, 'prec', 'rec')
        if (is.null(dev.list())) {
            png(save_name,
                width=7,
                height=5,
                units='in',
                res=300)
            plot(perf,
                 main = title,
                 xlim=c(0,1),
                 ylim=c(0,1),
                 col = i,
                 lwd = 2)
        } else {
            plot(perf,
                 main = title,
                 xlim=c(0,1),
                 ylim=c(0,1),
                 col = i,
                 lwd = 2,
                 add = TRUE)
        }
    }
    abline(a = n_black/n_total,
           b = 0,
           col = 'red',
           lty = 2,
           lwd = 2)
    legend('bottomleft',
           names,
           lwd = 2,
           col = seq_along(predictions),
           bty = 'n',
           inset = c(0.1, 0.07))
    legend('bottomleft',
           paste('Null AUPR:', sprintf('%.3f', round(n_black / n_total, 3))),
           lwd = 2,
           lty = 2,
           col = 'red',
           bty = 'n',
           inset = c(0.1, 0.0))
}

Figures for Paper

aupr_figures(list(image_level$filter_0, image_level$filter_50, image_level$filter_200, image_level$filter_240),
             image_level$label,
             'Precision-Recall Curves of Thresholded RVMs',
             c('Threshold 0 AUPR:', 'Threshold 50 AUPR: ', 'Threshold 150 AUPR: ', 'Threshold 220 AUPR: '),
             './out/figures/thresholded_image.png')
aupr_figures(list(image_level$filter_0_binarized, image_level$filter_50_binarized, image_level$filter_200_binarized, image_level$filter_240_binarized),
             image_level$label,
             'Precision-Recall Curves of Binarized RVMs',
             c('Threshold 0 AUPR:', 'Threshold 50 AUPR: ', 'Threshold 150 AUPR: ', 'Threshold 220 AUPR: '),
             './out/figures/binarized_image.png')
aupr_figures(list(image_level$filter_0_skeletonized, image_level$filter_50_skeletonized, image_level$filter_200_skeletonized, image_level$filter_240_skeletonized),
             image_level$label,
             'Precision-Recall Curves of Skeletonized RVMs',
             c('Threshold 0 AUPR:', 'Threshold 50 AUPR: ', 'Threshold 150 AUPR: ', 'Threshold 220 AUPR: '),
             './out/figures/skeletonized_image.png')
aupr_figures(list(subject_level$filter_0, subject_level$filter_50, subject_level$filter_200, subject_level$filter_240),
             subject_level$label,
             'Precision-Recall Curves of Thresholded RVMs',
             c('Threshold 0 AUPR:', 'Threshold 50 AUPR: ', 'Threshold 150 AUPR: ', 'Threshold 220 AUPR: '),
             './out/figures/thresholded_subject.png')
aupr_figures(list(subject_level$filter_0_binarized, subject_level$filter_50_binarized, subject_level$filter_200_binarized, subject_level$filter_240_binarized),
             subject_level$label,
             'Precision-Recall Curves of Binarized RVMs',
             c('Threshold 0 AUPR:', 'Threshold 50 AUPR: ', 'Threshold 150 AUPR: ', 'Threshold 220 AUPR: '),
             './out/figures/binarized_subject.png')
aupr_figures(list(subject_level$filter_0_skeletonized, subject_level$filter_50_skeletonized, subject_level$filter_200_skeletonized, subject_level$filter_240_skeletonized),
             subject_level$label,
             'Precision-Recall Curves of Skeletonized RVMs',
             c('Threshold 0 AUPR:', 'Threshold 50 AUPR: ', 'Threshold 150 AUPR: ', 'Threshold 220 AUPR: '),
             './out/figures/skeletonized_subject.png')

Evaluate on Manual Segmentations

manual_binary_0 <- read_csv('./out/probabilities/manual_segmentations_binarized_0.csv') %>%
    mutate(img_name = basename(img_loc)) %>%
    separate(img_name, c('site', 'subject_id')) %>%
    filter(!is.na(as.numeric(subject_id))) %>%
    mutate(subject_id = paste(toupper(site), subject_id, sep = '-')) %>%
    select(subject_id, probability) %>%
    inner_join(irop_data) %>%
    filter(race == 'African American' | race == 'Caucasian/White') %>%
    mutate(race = if_else(race == 'African American', 1, 0),
           across(race, as.factor))

── Column specification ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
cols(
  img_loc = col_character(),
  probability = col_double()
)
Warning: Expected 2 pieces. Additional pieces discarded in 196 rows [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, ...].
Warning in mask$eval_all_filter(dots, env_filter) :
  NAs introduced by coercion
Joining, by = "subject_id"
manual_skeleton_0 <- read_csv('./out/probabilities/manual_segmentations_skeletonized_0.csv') %>%
    mutate(img_name = basename(img_loc)) %>%
    separate(img_name, c('site', 'subject_id')) %>%
    filter(!is.na(as.numeric(subject_id))) %>%
    mutate(subject_id = paste(toupper(site), subject_id, sep = '-')) %>%
    select(subject_id, probability) %>%
    inner_join(irop_data) %>%
    filter(race == 'African American' | race == 'Caucasian/White') %>%
    mutate(race = if_else(race == 'African American', 1, 0),
           across(race, as.factor))

── Column specification ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
cols(
  img_loc = col_character(),
  probability = col_double()
)
Warning: Expected 2 pieces. Additional pieces discarded in 196 rows [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, ...].
Warning in mask$eval_all_filter(dots, env_filter) :
  NAs introduced by coercion
Joining, by = "subject_id"
manual_binary_50 <- read_csv('./out/probabilities/manual_segmentations_binarized_50.csv') %>%
    mutate(img_name = basename(img_loc)) %>%
    separate(img_name, c('site', 'subject_id')) %>%
    filter(!is.na(as.numeric(subject_id))) %>%
    mutate(subject_id = paste(toupper(site), subject_id, sep = '-')) %>%
    select(subject_id, probability) %>%
    inner_join(irop_data) %>%
    filter(race == 'African American' | race == 'Caucasian/White') %>%
    mutate(race = if_else(race == 'African American', 1, 0),
           across(race, as.factor))

── Column specification ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
cols(
  img_loc = col_character(),
  probability = col_double()
)
Warning: Expected 2 pieces. Additional pieces discarded in 196 rows [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, ...].
Warning in mask$eval_all_filter(dots, env_filter) :
  NAs introduced by coercion
Joining, by = "subject_id"
manual_skeleton_50 <- read_csv('./out/probabilities/manual_segmentations_skeletonized_50.csv') %>%
    mutate(img_name = basename(img_loc)) %>%
    separate(img_name, c('site', 'subject_id')) %>%
    filter(!is.na(as.numeric(subject_id))) %>%
    mutate(subject_id = paste(toupper(site), subject_id, sep = '-')) %>%
    select(subject_id, probability) %>%
    inner_join(irop_data) %>%
    filter(race == 'African American' | race == 'Caucasian/White') %>%
    mutate(race = if_else(race == 'African American', 1, 0),
           across(race, as.factor))

── Column specification ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
cols(
  img_loc = col_character(),
  probability = col_double()
)
Warning: Expected 2 pieces. Additional pieces discarded in 196 rows [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, ...].
Warning in mask$eval_all_filter(dots, env_filter) :
  NAs introduced by coercion
Joining, by = "subject_id"
pred <- prediction(manual_binary_0$probability, manual_binary_0$race)

num_yes <- length(manual_binary_0$race[manual_binary_0$race == 1])
num_no <- length(manual_binary_0$race[manual_binary_0$race == 0])

perf <- performance(pred,'prec', 'rec')
plot(perf, colorize=TRUE, main = 'Manual Segmentations: Binarized - 0 Threshold Model', ylim = c(0,1))
abline(a = num_yes / num_no, b = 0, col = 'red', lty = 2)
aupr <- performance(pred, measure = 'aucpr')
aupr <- aupr@y.values[[1]]
text(0.8, 0.9, paste('AUPR: ', sprintf('%.3f', round(aupr, 3)), sep = ''))

pred <- prediction(manual_skeleton_0$probability, manual_skeleton_0$race)

num_yes <- length(manual_skeleton_0$race[manual_skeleton_0$race == 1])
num_no <- length(manual_skeleton_0$race[manual_skeleton_0$race == 0])

perf <- performance(pred,'prec', 'rec')
plot(perf, colorize=TRUE, main = 'Manual Segmentations: Skeletonized - 0 Threshold Model', ylim = c(0,1))
abline(a = num_yes / num_no, b = 0, col = 'red', lty = 2)
aupr <- performance(pred, measure = 'aucpr')
aupr <- aupr@y.values[[1]]
text(0.8, 0.9, paste('AUPR: ', sprintf('%.3f', round(aupr, 3)), sep = ''))

pred <- prediction(manual_binary_50$probability, manual_binary_50$race)

num_yes <- length(manual_binary_50$race[manual_binary_50$race == 1])
num_no <- length(manual_binary_50$race[manual_binary_50$race == 0])

perf <- performance(pred,'prec', 'rec')
plot(perf, colorize=TRUE, main = 'Manual Segmentations: Binarized - 50 Threshold Model', ylim = c(0,1))
abline(a = num_yes / num_no, b = 0, col = 'red', lty = 2)
aupr <- performance(pred, measure = 'aucpr')
aupr <- aupr@y.values[[1]]
text(0.8, 0.9, paste('AUPR: ', sprintf('%.3f', round(aupr, 3)), sep = ''))

pred <- prediction(manual_skeleton_50$probability, manual_skeleton_50$race)

num_yes <- length(manual_skeleton_50$race[manual_skeleton_50$race == 1])
num_no <- length(manual_skeleton_50$race[manual_skeleton_50$race == 0])

perf <- performance(pred,'prec', 'rec')
plot(perf, colorize=TRUE, main = 'Manual Segmentations: Skeletonized - 50 Threshold Model', ylim = c(0,1))
abline(a = num_yes / num_no, b = 0, col = 'red', lty = 2)
aupr <- performance(pred, measure = 'aucpr')
aupr <- aupr@y.values[[1]]
text(0.8, 0.9, paste('AUPR: ', sprintf('%.3f', round(aupr, 3)), sep = ''))

image_level <- read_csv('./out/probabilities/retcam_filtered_0.csv', col_types = cols()) %>%
    mutate(label = as.factor(if_else(str_detect(img_loc, 'black'), 1, 0)),
           image_id = as.numeric(file_path_sans_ext(basename(img_loc)))) %>%
    select(img_loc, image_id, label, retcam = probability) %>%
    bind_cols(select(read_csv('./out/probabilities/segmentations_binarized_test.csv', col_types = cols()), filter_0_binarized = probability))
pred <- prediction(image_level$filter_0_binarized, image_level$label)

num_yes <- length(image_level$label[image_level$label == 1])
num_no <- length(image_level$label[image_level$label == 0])

perf <- performance(pred,'prec', 'rec')
plot(perf, colorize=TRUE, main = 'Manual Segmentations: Skeletonized - 0 Threshold Model', ylim = c(0,1))
abline(a = num_yes / num_no, b = 0, col = 'red', lty = 2)
aupr <- performance(pred, measure = 'aucpr')
aupr <- aupr@y.values[[1]]
text(0.8, 0.9, paste('AUPR: ', sprintf('%.3f', round(aupr, 3)), sep = ''))

---
title: 'Evaluate Models'
author: 'Author: Aaron S Coyner, PhD'
date: 'Last update: `r Sys.Date()`'
output:
    html_notebook:
        toc: yes
        toc_float: yes
        toc_depth: 3
---


# Setup
```{r}
library(tidyverse)
library(janitor)
library(tools)
library(caret)
library(ROCR)
library(caret)
```


```{r}
compute_auroc <- function(predictions, labels, title) {
    pred <- prediction(predictions, labels)
    auroc <- performance(pred, measure = 'auc')
    auroc <- auroc@y.values[[1]]
    perf <- performance(pred, 'tpr', 'fpr')
    plot(perf,
         main = title,
         xlim=c(0,1),
         ylim=c(0,1),
         colorize = FALSE)
    abline(a = 0,
           b = 1,
           col = 'red',
           lty = 2)
    text(0.8,
         0.25,
         paste('AUROC:', sprintf('%.3f', round(auroc, 3))))
}
```


```{r}
compute_aupr <- function(predictions, labels, title) {
    n_black <- length(labels[labels == 1])
    n_total <- length(labels)
    pred <- prediction(predictions, labels)
    aupr <- performance(pred, measure = 'aucpr')
    aupr <- aupr@y.values[[1]]
    perf <- performance(pred, 'prec', 'rec')
    plot(perf,
         main = title,
         xlim=c(0,1),
         ylim=c(0,1),
         colorize = FALSE)
    abline(a = n_black/n_total,
           b = 0,
           col = 'red',
           lty = 2)
    text(0.2,
         0.2,
         paste('Null AUPR:', sprintf('%.3f', round(n_black / n_total, 3))))
    text(0.8,
         0.2,
         paste('AUPR:', sprintf('%.3f', round(aupr, 3))))
}
```


```{r}
confusion_matrix <- function(df, column, threshold=0.5) {
    preds <- as.factor(if_else(df[column] >= threshold, 1, 0))
    confusionMatrix(preds, df$label, positive = '1')
}
```



# Load data
```{r}
irop_data <- read_csv('/Volumes/External/irop_data/irop_07092020.csv') %>%
    clean_names() %>%
    distinct(subject_id, .keep_all = TRUE) %>%
    select(subject_id, race)

test_data <- read_csv('./out/datasets/test_data.csv') %>%
    select(subject_id, image_id)


image_level <- read_csv('./out/probabilities/retcam_filtered_0.csv', col_types = cols()) %>%
    mutate(label = as.factor(if_else(str_detect(img_loc, 'black'), 1, 0)),
           image_id = as.numeric(file_path_sans_ext(basename(img_loc)))) %>%
    select(img_loc, image_id, label, retcam = probability) %>%
    bind_cols(select(read_csv('./out/probabilities/retcam_filtered_0_random.csv', col_types = cols()), retcam_random = probability)) %>%
    
    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_0_random.csv', col_types = cols()), filter_0_random = probability)) %>%
    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_0.csv', col_types = cols()), filter_0 = probability)) %>%
    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_0_binarized.csv', col_types = cols()), filter_0_binarized = probability)) %>%
    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_0_skeletonized.csv', col_types = cols()), filter_0_skeletonized = probability)) %>%
    
    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_50.csv', col_types = cols()), filter_50 = probability)) %>%
    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_50_binarized.csv', col_types = cols()), filter_50_binarized = probability)) %>%
    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_50_skeletonized.csv', col_types = cols()), filter_50_skeletonized = probability)) %>%

    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_100.csv', col_types = cols()), filter_100 = probability)) %>%
    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_100_binarized.csv', col_types = cols()), filter_100_binarized = probability)) %>%
    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_100_skeletonized.csv', col_types = cols()), filter_100_skeletonized = probability)) %>%

    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_150.csv', col_types = cols()), filter_150 = probability)) %>%
    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_150_binarized.csv', col_types = cols()), filter_150_binarized = probability)) %>%
    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_150_skeletonized.csv', col_types = cols()), filter_150_skeletonized = probability)) %>%

    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_200.csv', col_types = cols()), filter_200 = probability)) %>%
    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_200_binarized.csv', col_types = cols()), filter_200_binarized = probability)) %>%
    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_200_skeletonized.csv', col_types = cols()), filter_200_skeletonized = probability)) %>%

    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_210.csv', col_types = cols()), filter_210 = probability)) %>%
    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_210_binarized.csv', col_types = cols()), filter_210_binarized = probability)) %>%
    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_210_skeletonized.csv', col_types = cols()), filter_210_skeletonized = probability)) %>%

    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_220.csv', col_types = cols()), filter_220 = probability)) %>%
    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_220_binarized.csv', col_types = cols()), filter_220_binarized = probability)) %>%
    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_220_skeletonized.csv', col_types = cols()), filter_220_skeletonized = probability)) %>%

    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_230.csv', col_types = cols()), filter_230 = probability)) %>%
    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_230_binarized.csv', col_types = cols()), filter_230_binarized = probability)) %>%
    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_230_skeletonized.csv', col_types = cols()), filter_230_skeletonized = probability)) %>%

    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_240.csv', col_types = cols()), filter_240 = probability)) %>%
    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_240_binarized.csv', col_types = cols()), filter_240_binarized = probability)) %>%
    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_240_skeletonized.csv', col_types = cols()), filter_240_skeletonized = probability)) %>%

    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_250.csv', col_types = cols()), filter_250 = probability)) %>%
    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_250_binarized.csv', col_types = cols()), filter_250_binarized = probability)) %>%
    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_250_skeletonized.csv', col_types = cols()), filter_250_skeletonized = probability)) %>%

    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_257.csv', col_types = cols()), filter_257 = probability)) %>%
    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_257_binarized.csv', col_types = cols()), filter_257_binarized = probability)) %>%
    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_257_skeletonized.csv', col_types = cols()), filter_257_skeletonized = probability)) %>%

    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_0_10.csv', col_types = cols()), filter_10 = probability)) %>%
    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_0_10_binarized.csv', col_types = cols()), filter_10_binarized = probability)) %>%
    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_0_10_skeletonized.csv', col_types = cols()), filter_10_skeletonized = probability)) %>%

    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_75_150.csv', col_types = cols()), filter_75 = probability)) %>%
    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_75_150_binarized.csv', col_types = cols()), filter_75_binarized = probability)) %>%
    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_75_150_skeletonized.csv', col_types = cols()), filter_75_skeletonized = probability)) %>%
    
    inner_join(test_data, by = 'image_id') %>%
    select(subject_id, everything(), -img_loc)



subject_level <- image_level %>%
    group_by(subject_id) %>%
    mutate(across(c(-image_id, -label), median)) %>%
    ungroup() %>%
    distinct(subject_id, .keep_all = TRUE)
```


# Image-level Analysis

### RetCam Images

```{r}
compute_aupr(image_level$retcam, image_level$label, 'PR: Raw RetCam Images')
compute_auroc(image_level$retcam, image_level$label, 'ROC: Raw RetCam Images')
confusion_matrix(image_level, 'retcam')
```

```{r}
compute_aupr(image_level$retcam_random, image_level$label, 'PR: Raw RetCam Images - Shuffled Labels')
compute_auroc(image_level$retcam_random, image_level$label, 'ROC: Raw RetCam Images - Shuffled Labels')
confusion_matrix(image_level, 'retcam_random')
```



### Segmentations: zero all pixels < 0

```{r}
compute_aupr(image_level$filter_0, image_level$label, 'PR: Segmentations: zero all pixels < 0')
compute_auroc(image_level$filter_0, image_level$label, 'ROC: Segmentations: zero all pixels < 0')
confusion_matrix(image_level, 'filter_0')
```

```{r}
compute_aupr(image_level$filter_0_binarized, image_level$label, 'PR: Segmentations: zero all pixels < 0 and binarize')
compute_auroc(image_level$filter_0_binarized, image_level$label, 'ROC: Segmentations: zero all pixels < 0 and binarize')
confusion_matrix(image_level, 'filter_0_binarized')
```

```{r}
compute_aupr(image_level$filter_0_skeletonized, image_level$label, 'PR: Segmentations: zero all pixels < 0 and skeletonize')
compute_auroc(image_level$filter_0_skeletonized, image_level$label, 'ROC: Segmentations: zero all pixels < 0 and skeletonize')
confusion_matrix(image_level, 'filter_0_skeletonized')
```

```{r}
compute_aupr(image_level$filter_0_random, image_level$label, 'PR: Segmentations: zero all pixels < 0 - Shuffled Labels')
compute_auroc(image_level$filter_0_random, image_level$label, 'ROC: Segmentations: zero all pixels < 0 - Shuffled Labels')
confusion_matrix(image_level, 'filter_0_random')
```


### Segmentations: zero all pixels < 50

```{r}
compute_aupr(image_level$filter_50, image_level$label, 'PR: Segmentations: zero all pixels < 50')
compute_auroc(image_level$filter_50, image_level$label, 'ROC: Segmentations: zero all pixels < 50')
confusion_matrix(image_level, 'filter_50')
```

```{r}
compute_aupr(image_level$filter_50_binarized, image_level$label, 'PR: Segmentations: zero all pixels < 50 and binarize')
compute_auroc(image_level$filter_50_binarized, image_level$label, 'ROC: Segmentations: zero all pixels < 50 and binarize')
confusion_matrix(image_level, 'filter_0_binarized')
```

```{r}
compute_aupr(image_level$filter_50_skeletonized, image_level$label, 'PR: Segmentations: zero all pixels < 50 and skeletonize')
compute_auroc(image_level$filter_50_skeletonized, image_level$label, 'ROC: Segmentations: zero all pixels < 50 and skeletonize')
confusion_matrix(image_level, 'filter_50_skeletonized')
```



### Segmentations: zero all pixels < 100

```{r}
compute_aupr(image_level$filter_100, image_level$label, 'PR: Segmentations: zero all pixels < 100')
compute_auroc(image_level$filter_100, image_level$label, 'ROC: Segmentations: zero all pixels < 100')
confusion_matrix(image_level, 'filter_100')
```

```{r}
compute_aupr(image_level$filter_100_binarized, image_level$label, 'PR: Segmentations: zero all pixels < 100 and binarize')
compute_auroc(image_level$filter_100_binarized, image_level$label, 'ROC: Segmentations: zero all pixels < 100 and binarize')
confusion_matrix(image_level, 'filter_100_binarized')
```

```{r}
compute_aupr(image_level$filter_100_skeletonized, image_level$label, 'PR: Segmentations: zero all pixels < 100 and skeletonize')
compute_auroc(image_level$filter_100_skeletonized, image_level$label, 'ROC: Segmentations: zero all pixels < 100 and skeletonize')
confusion_matrix(image_level, 'filter_100_skeletonized')
```



### Segmentations: zero all pixels < 150

```{r}
compute_aupr(image_level$filter_150, image_level$label, 'PR: Segmentations: zero all pixels < 150')
compute_auroc(image_level$filter_150, image_level$label, 'ROC: Segmentations: zero all pixels < 150')
confusion_matrix(image_level, 'filter_150')
```

```{r}
compute_aupr(image_level$filter_150_binarized, image_level$label, 'PR: Segmentations: zero all pixels < 150 and binarize')
compute_auroc(image_level$filter_150_binarized, image_level$label, 'ROC: Segmentations: zero all pixels < 150 and binarize')
confusion_matrix(image_level, 'filter_150_binarized')
```

```{r}
compute_aupr(image_level$filter_150_skeletonized, image_level$label, 'PR: Segmentations: zero all pixels < 150 and skeletonize')
compute_auroc(image_level$filter_150_skeletonized, image_level$label, 'ROC: Segmentations: zero all pixels < 150 and skeletonize')
confusion_matrix(image_level, 'filter_150_skeletonized')
```



### Segmentations: zero all pixels < 200

```{r}
compute_aupr(image_level$filter_200, image_level$label, 'PR: Segmentations: zero all pixels < 200')
compute_auroc(image_level$filter_200, image_level$label, 'ROC: Segmentations: zero all pixels < 200')
confusion_matrix(image_level, 'filter_200')
```

```{r}
compute_aupr(image_level$filter_200_binarized, image_level$label, 'PR: Segmentations: zero all pixels < 200 and binarize')
compute_auroc(image_level$filter_200_binarized, image_level$label, 'ROC: Segmentations: zero all pixels < 200 and binarize')
confusion_matrix(image_level, 'filter_200_binarized')
```

```{r}
compute_aupr(image_level$filter_200_skeletonized, image_level$label, 'PR: Segmentations: zero all pixels < 200 and skeletonize')
compute_auroc(image_level$filter_200_skeletonized, image_level$label, 'ROC: Segmentations: zero all pixels < 200 and skeletonize')
confusion_matrix(image_level, 'filter_200_skeletonized')
```



### Segmentations: zero all pixels < 210

```{r}
compute_aupr(image_level$filter_210, image_level$label, 'PR: Segmentations: zero all pixels < 210')
compute_auroc(image_level$filter_210, image_level$label, 'ROC: Segmentations: zero all pixels < 210')
confusion_matrix(image_level, 'filter_210')
```

```{r}
compute_aupr(image_level$filter_210_binarized, image_level$label, 'PR: Segmentations: zero all pixels < 210 and binarize')
compute_auroc(image_level$filter_210_binarized, image_level$label, 'ROC: Segmentations: zero all pixels < 210 and binarize')
confusion_matrix(image_level, 'filter_210_binarized')
```

```{r}
compute_aupr(image_level$filter_210_skeletonized, image_level$label, 'PR: Segmentations: zero all pixels < 210 and skeletonize')
compute_auroc(image_level$filter_210_skeletonized, image_level$label, 'ROC: Segmentations: zero all pixels < 210 and skeletonize')
confusion_matrix(image_level, 'filter_210_skeletonized')
```



### Segmentations: zero all pixels < 220

```{r}
compute_aupr(image_level$filter_220, image_level$label, 'PR: Segmentations: zero all pixels < 220')
compute_auroc(image_level$filter_220, image_level$label, 'ROC: Segmentations: zero all pixels < 220')
confusion_matrix(image_level, 'filter_220')
```

```{r}
compute_aupr(image_level$filter_220_binarized, image_level$label, 'PR: Segmentations: zero all pixels < 220 and binarize')
compute_auroc(image_level$filter_220_binarized, image_level$label, 'ROC: Segmentations: zero all pixels < 220 and binarize')
confusion_matrix(image_level, 'filter_220_binarized')
```

```{r}
compute_aupr(image_level$filter_220_skeletonized, image_level$label, 'PR: Segmentations: zero all pixels < 220 and skeletonize')
compute_auroc(image_level$filter_220_skeletonized, image_level$label, 'ROC: Segmentations: zero all pixels < 220 and skeletonize')
confusion_matrix(image_level, 'filter_220_skeletonized')
```



### Segmentations: zero all pixels < 230

```{r}
compute_aupr(image_level$filter_230, image_level$label, 'PR: Segmentations: zero all pixels < 230')
compute_auroc(image_level$filter_230, image_level$label, 'ROC: Segmentations: zero all pixels < 230')
confusion_matrix(image_level, 'filter_230')
```

```{r}
compute_aupr(image_level$filter_230_binarized, image_level$label, 'PR: Segmentations: zero all pixels < 230 and binarize')
compute_auroc(image_level$filter_230_binarized, image_level$label, 'ROC: Segmentations: zero all pixels < 230 and binarize')
confusion_matrix(image_level, 'filter_230_binarized')
```

```{r}
compute_aupr(image_level$filter_230_skeletonized, image_level$label, 'PR: Segmentations: zero all pixels < 230 and skeletonize')
compute_auroc(image_level$filter_230_skeletonized, image_level$label, 'ROC: Segmentations: zero all pixels < 230 and skeletonize')
confusion_matrix(image_level, 'filter_230_skeletonized')
```



### Segmentations: zero all pixels < 240

```{r}
compute_aupr(image_level$filter_240, image_level$label, 'PR: Segmentations: zero all pixels < 240')
compute_auroc(image_level$filter_240, image_level$label, 'ROC: Segmentations: zero all pixels < 240')
confusion_matrix(image_level, 'filter_240')
```

```{r}
compute_aupr(image_level$filter_240_binarized, image_level$label, 'PR: Segmentations: zero all pixels < 240 and binarize')
compute_auroc(image_level$filter_240_binarized, image_level$label, 'ROC: Segmentations: zero all pixels < 240 and binarize')
confusion_matrix(image_level, 'filter_240_binarized')
```

```{r}
compute_aupr(image_level$filter_240_skeletonized, image_level$label, 'PR: Segmentations: zero all pixels < 240 and skeletonize')
compute_auroc(image_level$filter_240_skeletonized, image_level$label, 'ROC: Segmentations: zero all pixels < 240 and skeletonize')
confusion_matrix(image_level, 'filter_240_skeletonized')
```



### Segmentations: zero all pixels < 250

```{r}
compute_aupr(image_level$filter_250, image_level$label, 'PR: Segmentations: zero all pixels < 250')
compute_auroc(image_level$filter_250, image_level$label, 'ROC: Segmentations: zero all pixels < 250')
confusion_matrix(image_level, 'filter_250')
```

```{r}
compute_aupr(image_level$filter_250_binarized, image_level$label, 'PR: Segmentations: zero all pixels < 250 and binarize')
compute_auroc(image_level$filter_250_binarized, image_level$label, 'ROC: Segmentations: zero all pixels < 250 and binarize')
confusion_matrix(image_level, 'filter_250_binarized')
```

```{r}
compute_aupr(image_level$filter_250_skeletonized, image_level$label, 'PR: Segmentations: zero all pixels < 250 and skeletonize')
compute_auroc(image_level$filter_250_skeletonized, image_level$label, 'ROC: Segmentations: zero all pixels < 250 and skeletonize')
confusion_matrix(image_level, 'filter_250_skeletonized')
```



### Segmentations: zero all pixels < 257

```{r}
compute_aupr(image_level$filter_257, image_level$label, 'PR: Segmentations: zero all pixels < 257')
compute_auroc(image_level$filter_257, image_level$label, 'ROC: Segmentations: zero all pixels < 257')
confusion_matrix(image_level, 'filter_257')
```

```{r}
compute_aupr(image_level$filter_257_binarized, image_level$label, 'PR: Segmentations: zero all pixels < 257 and binarize')
compute_auroc(image_level$filter_257_binarized, image_level$label, 'ROC: Segmentations: zero all pixels < 257 and binarize')
confusion_matrix(image_level, 'filter_257_binarized')
```

```{r}
compute_aupr(image_level$filter_257_skeletonized, image_level$label, 'PR: Segmentations: zero all pixels < 257 and skeletonize')
compute_auroc(image_level$filter_257_skeletonized, image_level$label, 'ROC: Segmentations: zero all pixels < 257 and skeletonize')
confusion_matrix(image_level, 'filter_257_skeletonized')
```



### Segmentations: zero all pixels > 10

```{r}
compute_aupr(image_level$filter_10, image_level$label, 'PR: Segmentations: zero all pixels < 10')
compute_auroc(image_level$filter_10, image_level$label, 'ROC: Segmentations: zero all pixels < 10')
confusion_matrix(image_level, 'filter_10')
```

```{r}
compute_aupr(image_level$filter_10_binarized, image_level$label, 'PR: Segmentations: zero all pixels < 10 and binarize')
compute_auroc(image_level$filter_10_binarized, image_level$label, 'ROC: Segmentations: zero all pixels < 10 and binarize')
confusion_matrix(image_level, 'filter_10_binarized')
```

```{r}
compute_aupr(image_level$filter_10_skeletonized, image_level$label, 'PR: Segmentations: zero all pixels < 10 and skeletonize')
compute_auroc(image_level$filter_10_skeletonized, image_level$label, 'ROC: Segmentations: zero all pixels < 10 and skeletonize')
confusion_matrix(image_level, 'filter_10_skeletonized')
```



### Segmentations: zero all pixels < 75 and > 150

```{r}
compute_aupr(image_level$filter_75, image_level$label, 'PR: Segmentations: zero all pixels < 75 and > 150')
compute_auroc(image_level$filter_75, image_level$label, 'ROC: Segmentations: zero all pixels < 75 and > 150')
confusion_matrix(image_level, 'filter_75')
```

```{r}
compute_aupr(image_level$filter_75_binarized, image_level$label, 'PR: Segmentations: zero all pixels < 75 and > 150 and binarize')
compute_auroc(image_level$filter_75_binarized, image_level$label, 'ROC: Segmentations: zero all pixels < 75 and > 150 and binarize')
confusion_matrix(image_level, 'filter_75_binarized')
```

```{r}
compute_aupr(image_level$filter_75_skeletonized, image_level$label, 'PR: Segmentations: zero all pixels < 75 and > 150 and skeletonize')
compute_auroc(image_level$filter_75_skeletonized, image_level$label, 'ROC: Segmentations: zero all pixels < 75 and > 150 and skeletonize')
confusion_matrix(image_level, 'filter_75_skeletonized')
```











# Subject-level Analysis

### RetCam Images

```{r}
compute_aupr(subject_level$retcam, subject_level$label, 'PR: Raw RetCam Images')
compute_auroc(subject_level$retcam, subject_level$label, 'ROC: Raw RetCam Images')
confusion_matrix(subject_level, 'retcam')
```

```{r}
compute_aupr(subject_level$retcam_random, subject_level$label, 'PR: Raw RetCam Images - Shuffled Labels')
compute_auroc(subject_level$retcam_random, subject_level$label, 'ROC: Raw RetCam Images - Shuffled Labels')
confusion_matrix(subject_level, 'retcam_random')
```



### Segmentations: zero all pixels < 0

```{r}
compute_aupr(subject_level$filter_0, subject_level$label, 'PR: Segmentations: zero all pixels < 0')
compute_auroc(subject_level$filter_0, subject_level$label, 'ROC: Segmentations: zero all pixels < 0')
confusion_matrix(subject_level, 'filter_0')
```

```{r}
compute_aupr(subject_level$filter_0_binarized, subject_level$label, 'PR: Segmentations: zero all pixels < 0 and binarize')
compute_auroc(subject_level$filter_0_binarized, subject_level$label, 'ROC: Segmentations: zero all pixels < 0 and binarize')
confusion_matrix(subject_level, 'filter_0_binarized')
```

```{r}
compute_aupr(subject_level$filter_0_skeletonized, subject_level$label, 'PR: Segmentations: zero all pixels < 0 and skeletonize')
compute_auroc(subject_level$filter_0_skeletonized, subject_level$label, 'ROC: Segmentations: zero all pixels < 0 and skeletonize')
confusion_matrix(subject_level, 'filter_0_skeletonized')
```

```{r}
compute_aupr(subject_level$filter_0_random, subject_level$label, 'PR: Segmentations: zero all pixels < 0 - Shuffled Labels')
compute_auroc(subject_level$filter_0_random, subject_level$label, 'ROC: Segmentations: zero all pixels < 0 - Shuffled Labels')
confusion_matrix(subject_level, 'filter_0_random')
```


### Segmentations: zero all pixels < 50

```{r}
compute_aupr(subject_level$filter_50, subject_level$label, 'PR: Segmentations: zero all pixels < 50')
compute_auroc(subject_level$filter_50, subject_level$label, 'ROC: Segmentations: zero all pixels < 50')
confusion_matrix(subject_level, 'filter_50')
```

```{r}
compute_aupr(subject_level$filter_50_binarized, subject_level$label, 'PR: Segmentations: zero all pixels < 50 and binarize')
compute_auroc(subject_level$filter_50_binarized, subject_level$label, 'ROC: Segmentations: zero all pixels < 50 and binarize')
confusion_matrix(subject_level, 'filter_0_binarized')
```

```{r}
compute_aupr(subject_level$filter_50_skeletonized, subject_level$label, 'PR: Segmentations: zero all pixels < 50 and skeletonize')
compute_auroc(subject_level$filter_50_skeletonized, subject_level$label, 'ROC: Segmentations: zero all pixels < 50 and skeletonize')
confusion_matrix(subject_level, 'filter_50_skeletonized')
```



### Segmentations: zero all pixels < 100

```{r}
compute_aupr(subject_level$filter_100, subject_level$label, 'PR: Segmentations: zero all pixels < 100')
compute_auroc(subject_level$filter_100, subject_level$label, 'ROC: Segmentations: zero all pixels < 100')
confusion_matrix(subject_level, 'filter_100')
```

```{r}
compute_aupr(subject_level$filter_100_binarized, subject_level$label, 'PR: Segmentations: zero all pixels < 100 and binarize')
compute_auroc(subject_level$filter_100_binarized, subject_level$label, 'ROC: Segmentations: zero all pixels < 100 and binarize')
confusion_matrix(subject_level, 'filter_100_binarized')
```

```{r}
compute_aupr(subject_level$filter_100_skeletonized, subject_level$label, 'PR: Segmentations: zero all pixels < 100 and skeletonize')
compute_auroc(subject_level$filter_100_skeletonized, subject_level$label, 'ROC: Segmentations: zero all pixels < 100 and skeletonize')
confusion_matrix(subject_level, 'filter_100_skeletonized')
```



### Segmentations: zero all pixels < 150

```{r}
compute_aupr(subject_level$filter_150, subject_level$label, 'PR: Segmentations: zero all pixels < 150')
compute_auroc(subject_level$filter_150, subject_level$label, 'ROC: Segmentations: zero all pixels < 150')
confusion_matrix(subject_level, 'filter_150')
```

```{r}
compute_aupr(subject_level$filter_150_binarized, subject_level$label, 'PR: Segmentations: zero all pixels < 150 and binarize')
compute_auroc(subject_level$filter_150_binarized, subject_level$label, 'ROC: Segmentations: zero all pixels < 150 and binarize')
confusion_matrix(subject_level, 'filter_150_binarized')
```

```{r}
compute_aupr(subject_level$filter_150_skeletonized, subject_level$label, 'PR: Segmentations: zero all pixels < 150 and skeletonize')
compute_auroc(subject_level$filter_150_skeletonized, subject_level$label, 'ROC: Segmentations: zero all pixels < 150 and skeletonize')
confusion_matrix(subject_level, 'filter_150_skeletonized')
```



### Segmentations: zero all pixels < 200

```{r}
compute_aupr(subject_level$filter_200, subject_level$label, 'PR: Segmentations: zero all pixels < 200')
compute_auroc(subject_level$filter_200, subject_level$label, 'ROC: Segmentations: zero all pixels < 200')
confusion_matrix(subject_level, 'filter_200')
```

```{r}
compute_aupr(subject_level$filter_200_binarized, subject_level$label, 'PR: Segmentations: zero all pixels < 200 and binarize')
compute_auroc(subject_level$filter_200_binarized, subject_level$label, 'ROC: Segmentations: zero all pixels < 200 and binarize')
confusion_matrix(subject_level, 'filter_200_binarized')
```

```{r}
compute_aupr(subject_level$filter_200_skeletonized, subject_level$label, 'PR: Segmentations: zero all pixels < 200 and skeletonize')
compute_auroc(subject_level$filter_200_skeletonized, subject_level$label, 'ROC: Segmentations: zero all pixels < 200 and skeletonize')
confusion_matrix(subject_level, 'filter_200_skeletonized')
```



### Segmentations: zero all pixels < 210

```{r}
compute_aupr(subject_level$filter_210, subject_level$label, 'PR: Segmentations: zero all pixels < 210')
compute_auroc(subject_level$filter_210, subject_level$label, 'ROC: Segmentations: zero all pixels < 210')
confusion_matrix(subject_level, 'filter_210')
```

```{r}
compute_aupr(subject_level$filter_210_binarized, subject_level$label, 'PR: Segmentations: zero all pixels < 210 and binarize')
compute_auroc(subject_level$filter_210_binarized, subject_level$label, 'ROC: Segmentations: zero all pixels < 210 and binarize')
confusion_matrix(subject_level, 'filter_210_binarized')
```

```{r}
compute_aupr(subject_level$filter_210_skeletonized, subject_level$label, 'PR: Segmentations: zero all pixels < 210 and skeletonize')
compute_auroc(subject_level$filter_210_skeletonized, subject_level$label, 'ROC: Segmentations: zero all pixels < 210 and skeletonize')
confusion_matrix(subject_level, 'filter_210_skeletonized')
```



### Segmentations: zero all pixels < 220

```{r}
compute_aupr(subject_level$filter_220, subject_level$label, 'PR: Segmentations: zero all pixels < 220')
compute_auroc(subject_level$filter_220, subject_level$label, 'ROC: Segmentations: zero all pixels < 220')
confusion_matrix(subject_level, 'filter_220')
```

```{r}
compute_aupr(subject_level$filter_220_binarized, subject_level$label, 'PR: Segmentations: zero all pixels < 220 and binarize')
compute_auroc(subject_level$filter_220_binarized, subject_level$label, 'ROC: Segmentations: zero all pixels < 220 and binarize')
confusion_matrix(subject_level, 'filter_220_binarized')
```

```{r}
compute_aupr(subject_level$filter_220_skeletonized, subject_level$label, 'PR: Segmentations: zero all pixels < 220 and skeletonize')
compute_auroc(subject_level$filter_220_skeletonized, subject_level$label, 'ROC: Segmentations: zero all pixels < 220 and skeletonize')
confusion_matrix(subject_level, 'filter_220_skeletonized')
```



### Segmentations: zero all pixels < 230

```{r}
compute_aupr(subject_level$filter_230, subject_level$label, 'PR: Segmentations: zero all pixels < 230')
compute_auroc(subject_level$filter_230, subject_level$label, 'ROC: Segmentations: zero all pixels < 230')
confusion_matrix(subject_level, 'filter_230')
```

```{r}
compute_aupr(subject_level$filter_230_binarized, subject_level$label, 'PR: Segmentations: zero all pixels < 230 and binarize')
compute_auroc(subject_level$filter_230_binarized, subject_level$label, 'ROC: Segmentations: zero all pixels < 230 and binarize')
confusion_matrix(subject_level, 'filter_230_binarized')
```

```{r}
compute_aupr(subject_level$filter_230_skeletonized, subject_level$label, 'PR: Segmentations: zero all pixels < 230 and skeletonize')
compute_auroc(subject_level$filter_230_skeletonized, subject_level$label, 'ROC: Segmentations: zero all pixels < 230 and skeletonize')
confusion_matrix(subject_level, 'filter_230_skeletonized')
```



### Segmentations: zero all pixels < 240

```{r}
compute_aupr(subject_level$filter_240, subject_level$label, 'PR: Segmentations: zero all pixels < 240')
compute_auroc(subject_level$filter_240, subject_level$label, 'ROC: Segmentations: zero all pixels < 240')
confusion_matrix(subject_level, 'filter_240')
```

```{r}
compute_aupr(subject_level$filter_240_binarized, subject_level$label, 'PR: Segmentations: zero all pixels < 240 and binarize')
compute_auroc(subject_level$filter_240_binarized, subject_level$label, 'ROC: Segmentations: zero all pixels < 240 and binarize')
confusion_matrix(subject_level, 'filter_240_binarized')
```

```{r}
compute_aupr(subject_level$filter_240_skeletonized, subject_level$label, 'PR: Segmentations: zero all pixels < 240 and skeletonize')
compute_auroc(subject_level$filter_240_skeletonized, subject_level$label, 'ROC: Segmentations: zero all pixels < 240 and skeletonize')
confusion_matrix(subject_level, 'filter_240_skeletonized')
```



### Segmentations: zero all pixels < 250

```{r}
compute_aupr(subject_level$filter_250, subject_level$label, 'PR: Segmentations: zero all pixels < 250')
compute_auroc(subject_level$filter_250, subject_level$label, 'ROC: Segmentations: zero all pixels < 250')
confusion_matrix(subject_level, 'filter_250')
```

```{r}
compute_aupr(subject_level$filter_250_binarized, subject_level$label, 'PR: Segmentations: zero all pixels < 250 and binarize')
compute_auroc(subject_level$filter_250_binarized, subject_level$label, 'ROC: Segmentations: zero all pixels < 250 and binarize')
confusion_matrix(subject_level, 'filter_250_binarized')
```

```{r}
compute_aupr(subject_level$filter_250_skeletonized, subject_level$label, 'PR: Segmentations: zero all pixels < 250 and skeletonize')
compute_auroc(subject_level$filter_250_skeletonized, subject_level$label, 'ROC: Segmentations: zero all pixels < 250 and skeletonize')
confusion_matrix(subject_level, 'filter_250_skeletonized')
```



### Segmentations: zero all pixels < 257

```{r}
compute_aupr(subject_level$filter_257, subject_level$label, 'PR: Segmentations: zero all pixels < 257')
compute_auroc(subject_level$filter_257, subject_level$label, 'ROC: Segmentations: zero all pixels < 257')
confusion_matrix(subject_level, 'filter_257')
```

```{r}
compute_aupr(subject_level$filter_257_binarized, subject_level$label, 'PR: Segmentations: zero all pixels < 257 and binarize')
compute_auroc(subject_level$filter_257_binarized, subject_level$label, 'ROC: Segmentations: zero all pixels < 257 and binarize')
confusion_matrix(subject_level, 'filter_257_binarized')
```

```{r}
compute_aupr(subject_level$filter_257_skeletonized, subject_level$label, 'PR: Segmentations: zero all pixels < 257 and skeletonize')
compute_auroc(subject_level$filter_257_skeletonized, subject_level$label, 'ROC: Segmentations: zero all pixels < 257 and skeletonize')
confusion_matrix(subject_level, 'filter_257_skeletonized')
```



### Segmentations: zero all pixels > 10

```{r}
compute_aupr(subject_level$filter_10, subject_level$label, 'PR: Segmentations: zero all pixels < 10')
compute_auroc(subject_level$filter_10, subject_level$label, 'ROC: Segmentations: zero all pixels < 10')
confusion_matrix(subject_level, 'filter_10')
```

```{r}
compute_aupr(subject_level$filter_10_binarized, subject_level$label, 'PR: Segmentations: zero all pixels < 10 and binarize')
compute_auroc(subject_level$filter_10_binarized, subject_level$label, 'ROC: Segmentations: zero all pixels < 10 and binarize')
confusion_matrix(subject_level, 'filter_10_binarized')
```

```{r}
compute_aupr(subject_level$filter_10_skeletonized, subject_level$label, 'PR: Segmentations: zero all pixels < 10 and skeletonize')
compute_auroc(subject_level$filter_10_skeletonized, subject_level$label, 'ROC: Segmentations: zero all pixels < 10 and skeletonize')
confusion_matrix(subject_level, 'filter_10_skeletonized')
```



### Segmentations: zero all pixels < 75 and > 150

```{r}
compute_aupr(subject_level$filter_75, subject_level$label, 'PR: Segmentations: zero all pixels < 75 and > 150')
compute_auroc(subject_level$filter_75, subject_level$label, 'ROC: Segmentations: zero all pixels < 75 and > 150')
confusion_matrix(subject_level, 'filter_75')
```

```{r}
compute_aupr(subject_level$filter_75_binarized, subject_level$label, 'PR: Segmentations: zero all pixels < 75 and > 150 and binarize')
compute_auroc(subject_level$filter_75_binarized, subject_level$label, 'ROC: Segmentations: zero all pixels < 75 and > 150 and binarize')
confusion_matrix(subject_level, 'filter_75_binarized')
```

```{r}
compute_aupr(subject_level$filter_75_skeletonized, subject_level$label, 'PR: Segmentations: zero all pixels < 75 and > 150 and skeletonize')
compute_auroc(subject_level$filter_75_skeletonized, subject_level$label, 'ROC: Segmentations: zero all pixels < 75 and > 150 and skeletonize')
confusion_matrix(subject_level, 'filter_75_skeletonized')
```



```{r}
aupr_figures <- function(predictions, labels, title, names, save_name) {
    n_black <- length(labels[labels == 1])
    n_total <- length(labels)
    for (i in seq_along(predictions)) {
        preds <- prediction(predictions[i], labels)
        aupr <- performance(preds, measure = 'aucpr')
        aupr <- aupr@y.values[[1]]
        perf <- performance(preds, 'prec', 'rec')
        if (is.null(dev.list())) {
            png(save_name,
                width=7,
                height=5,
                units='in',
                res=300)
            plot(perf,
                 main = title,
                 xlim=c(0,1),
                 ylim=c(0,1),
                 col = i,
                 lwd = 2)
        } else {
            plot(perf,
                 main = title,
                 xlim=c(0,1),
                 ylim=c(0,1),
                 col = i,
                 lwd = 2,
                 add = TRUE)
        }
    }
    abline(a = n_black/n_total,
           b = 0,
           col = 'red',
           lty = 2,
           lwd = 2)
    legend('bottomleft',
           names,
           lwd = 2,
           col = seq_along(predictions),
           bty = 'n',
           inset = c(0.1, 0.07))
    legend('bottomleft',
           paste('Null AUPR:', sprintf('%.3f', round(n_black / n_total, 3))),
           lwd = 2,
           lty = 2,
           col = 'red',
           bty = 'n',
           inset = c(0.1, 0.0))
}
```


## Figures for Paper
```{r}
aupr_figures(list(image_level$filter_0, image_level$filter_50, image_level$filter_200, image_level$filter_240),
             image_level$label,
             'Precision-Recall Curves of Thresholded RVMs',
             c('Threshold 0 AUPR:', 'Threshold 50 AUPR: ', 'Threshold 150 AUPR: ', 'Threshold 220 AUPR: '),
             './out/figures/thresholded_image.png')
```

```{r}
aupr_figures(list(image_level$filter_0_binarized, image_level$filter_50_binarized, image_level$filter_200_binarized, image_level$filter_240_binarized),
             image_level$label,
             'Precision-Recall Curves of Binarized RVMs',
             c('Threshold 0 AUPR:', 'Threshold 50 AUPR: ', 'Threshold 150 AUPR: ', 'Threshold 220 AUPR: '),
             './out/figures/binarized_image.png')
```

```{r}
aupr_figures(list(image_level$filter_0_skeletonized, image_level$filter_50_skeletonized, image_level$filter_200_skeletonized, image_level$filter_240_skeletonized),
             image_level$label,
             'Precision-Recall Curves of Skeletonized RVMs',
             c('Threshold 0 AUPR:', 'Threshold 50 AUPR: ', 'Threshold 150 AUPR: ', 'Threshold 220 AUPR: '),
             './out/figures/skeletonized_image.png')
```

```{r}
aupr_figures(list(subject_level$filter_0, subject_level$filter_50, subject_level$filter_200, subject_level$filter_240),
             subject_level$label,
             'Precision-Recall Curves of Thresholded RVMs',
             c('Threshold 0 AUPR:', 'Threshold 50 AUPR: ', 'Threshold 150 AUPR: ', 'Threshold 220 AUPR: '),
             './out/figures/thresholded_subject.png')
```

```{r}
aupr_figures(list(subject_level$filter_0_binarized, subject_level$filter_50_binarized, subject_level$filter_200_binarized, subject_level$filter_240_binarized),
             subject_level$label,
             'Precision-Recall Curves of Binarized RVMs',
             c('Threshold 0 AUPR:', 'Threshold 50 AUPR: ', 'Threshold 150 AUPR: ', 'Threshold 220 AUPR: '),
             './out/figures/binarized_subject.png')
```

```{r}
aupr_figures(list(subject_level$filter_0_skeletonized, subject_level$filter_50_skeletonized, subject_level$filter_200_skeletonized, subject_level$filter_240_skeletonized),
             subject_level$label,
             'Precision-Recall Curves of Skeletonized RVMs',
             c('Threshold 0 AUPR:', 'Threshold 50 AUPR: ', 'Threshold 150 AUPR: ', 'Threshold 220 AUPR: '),
             './out/figures/skeletonized_subject.png')
```



## Evaluate on Manual Segmentations
```{r}
manual_binary_0 <- read_csv('./out/probabilities/manual_segmentations_binarized_0.csv') %>%
    mutate(img_name = basename(img_loc)) %>%
    separate(img_name, c('site', 'subject_id')) %>%
    filter(!is.na(as.numeric(subject_id))) %>%
    mutate(subject_id = paste(toupper(site), subject_id, sep = '-')) %>%
    select(subject_id, probability) %>%
    inner_join(irop_data) %>%
    filter(race == 'African American' | race == 'Caucasian/White') %>%
    mutate(race = if_else(race == 'African American', 1, 0),
           across(race, as.factor))

manual_skeleton_0 <- read_csv('./out/probabilities/manual_segmentations_skeletonized_0.csv') %>%
    mutate(img_name = basename(img_loc)) %>%
    separate(img_name, c('site', 'subject_id')) %>%
    filter(!is.na(as.numeric(subject_id))) %>%
    mutate(subject_id = paste(toupper(site), subject_id, sep = '-')) %>%
    select(subject_id, probability) %>%
    inner_join(irop_data) %>%
    filter(race == 'African American' | race == 'Caucasian/White') %>%
    mutate(race = if_else(race == 'African American', 1, 0),
           across(race, as.factor))

manual_binary_50 <- read_csv('./out/probabilities/manual_segmentations_binarized_50.csv') %>%
    mutate(img_name = basename(img_loc)) %>%
    separate(img_name, c('site', 'subject_id')) %>%
    filter(!is.na(as.numeric(subject_id))) %>%
    mutate(subject_id = paste(toupper(site), subject_id, sep = '-')) %>%
    select(subject_id, probability) %>%
    inner_join(irop_data) %>%
    filter(race == 'African American' | race == 'Caucasian/White') %>%
    mutate(race = if_else(race == 'African American', 1, 0),
           across(race, as.factor))

manual_skeleton_50 <- read_csv('./out/probabilities/manual_segmentations_skeletonized_50.csv') %>%
    mutate(img_name = basename(img_loc)) %>%
    separate(img_name, c('site', 'subject_id')) %>%
    filter(!is.na(as.numeric(subject_id))) %>%
    mutate(subject_id = paste(toupper(site), subject_id, sep = '-')) %>%
    select(subject_id, probability) %>%
    inner_join(irop_data) %>%
    filter(race == 'African American' | race == 'Caucasian/White') %>%
    mutate(race = if_else(race == 'African American', 1, 0),
           across(race, as.factor))
```


```{r}
pred <- prediction(manual_binary_0$probability, manual_binary_0$race)

num_yes <- length(manual_binary_0$race[manual_binary_0$race == 1])
num_no <- length(manual_binary_0$race[manual_binary_0$race == 0])

perf <- performance(pred,'prec', 'rec')
plot(perf, colorize=TRUE, main = 'Manual Segmentations: Binarized - 0 Threshold Model', ylim = c(0,1))
abline(a = num_yes / num_no, b = 0, col = 'red', lty = 2)
aupr <- performance(pred, measure = 'aucpr')
aupr <- aupr@y.values[[1]]
text(0.8, 0.9, paste('AUPR: ', sprintf('%.3f', round(aupr, 3)), sep = ''))
```


```{r}
pred <- prediction(manual_skeleton_0$probability, manual_skeleton_0$race)

num_yes <- length(manual_skeleton_0$race[manual_skeleton_0$race == 1])
num_no <- length(manual_skeleton_0$race[manual_skeleton_0$race == 0])

perf <- performance(pred,'prec', 'rec')
plot(perf, colorize=TRUE, main = 'Manual Segmentations: Skeletonized - 0 Threshold Model', ylim = c(0,1))
abline(a = num_yes / num_no, b = 0, col = 'red', lty = 2)
aupr <- performance(pred, measure = 'aucpr')
aupr <- aupr@y.values[[1]]
text(0.8, 0.9, paste('AUPR: ', sprintf('%.3f', round(aupr, 3)), sep = ''))
```


```{r}
pred <- prediction(manual_binary_50$probability, manual_binary_50$race)

num_yes <- length(manual_binary_50$race[manual_binary_50$race == 1])
num_no <- length(manual_binary_50$race[manual_binary_50$race == 0])

perf <- performance(pred,'prec', 'rec')
plot(perf, colorize=TRUE, main = 'Manual Segmentations: Binarized - 50 Threshold Model', ylim = c(0,1))
abline(a = num_yes / num_no, b = 0, col = 'red', lty = 2)
aupr <- performance(pred, measure = 'aucpr')
aupr <- aupr@y.values[[1]]
text(0.8, 0.9, paste('AUPR: ', sprintf('%.3f', round(aupr, 3)), sep = ''))
```


```{r}
pred <- prediction(manual_skeleton_50$probability, manual_skeleton_50$race)

num_yes <- length(manual_skeleton_50$race[manual_skeleton_50$race == 1])
num_no <- length(manual_skeleton_50$race[manual_skeleton_50$race == 0])

perf <- performance(pred,'prec', 'rec')
plot(perf, colorize=TRUE, main = 'Manual Segmentations: Skeletonized - 50 Threshold Model', ylim = c(0,1))
abline(a = num_yes / num_no, b = 0, col = 'red', lty = 2)
aupr <- performance(pred, measure = 'aucpr')
aupr <- aupr@y.values[[1]]
text(0.8, 0.9, paste('AUPR: ', sprintf('%.3f', round(aupr, 3)), sep = ''))
```


```{r}
image_level <- read_csv('./out/probabilities/retcam_filtered_0.csv', col_types = cols()) %>%
    mutate(label = as.factor(if_else(str_detect(img_loc, 'black'), 1, 0)),
           image_id = as.numeric(file_path_sans_ext(basename(img_loc)))) %>%
    select(img_loc, image_id, label, retcam = probability) %>%
    bind_cols(select(read_csv('./out/probabilities/segmentations_binarized_test.csv', col_types = cols()), filter_0_binarized = probability))
```

```{r}
pred <- prediction(image_level$filter_0_binarized, image_level$label)

num_yes <- length(image_level$label[image_level$label == 1])
num_no <- length(image_level$label[image_level$label == 0])

perf <- performance(pred,'prec', 'rec')
plot(perf, colorize=TRUE, main = 'Manual Segmentations: Skeletonized - 0 Threshold Model', ylim = c(0,1))
abline(a = num_yes / num_no, b = 0, col = 'red', lty = 2)
aupr <- performance(pred, measure = 'aucpr')
aupr <- aupr@y.values[[1]]
text(0.8, 0.9, paste('AUPR: ', sprintf('%.3f', round(aupr, 3)), sep = ''))
```